diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 000000000..e16c2e461 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,6 @@ +[run] +omit = + */tests/* + */llama_stack/providers/* + */llama_stack/templates/* + .venv/* diff --git a/.github/workflows/install-script-ci.yml b/.github/workflows/install-script-ci.yml new file mode 100644 index 000000000..2eb234c77 --- /dev/null +++ b/.github/workflows/install-script-ci.yml @@ -0,0 +1,26 @@ +name: Installer CI + +on: + pull_request: + paths: + - 'install.sh' + push: + paths: + - 'install.sh' + schedule: + - cron: '0 2 * * *' # every day at 02:00 UTC + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2 + - name: Run ShellCheck on install.sh + run: shellcheck install.sh + smoke-test: + needs: lint + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2 + - name: Run installer end-to-end + run: ./install.sh diff --git a/.github/workflows/integration-auth-tests.yml b/.github/workflows/integration-auth-tests.yml new file mode 100644 index 000000000..3c155195f --- /dev/null +++ b/.github/workflows/integration-auth-tests.yml @@ -0,0 +1,136 @@ +name: Integration Auth Tests + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + paths: + - 'distributions/**' + - 'llama_stack/**' + - 'tests/integration/**' + - 'uv.lock' + - 'pyproject.toml' + - 'requirements.txt' + - '.github/workflows/integration-auth-tests.yml' # This workflow + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + test-matrix: + runs-on: ubuntu-latest + strategy: + matrix: + auth-provider: [kubernetes] + fail-fast: false # we want to run all tests regardless of failure + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + python-version: "3.10" + + - name: Set Up Environment and Install Dependencies + run: | + uv sync --extra dev --extra test + uv pip install -e . + llama stack build --template ollama --image-type venv + + - name: Install minikube + if: ${{ matrix.auth-provider == 'kubernetes' }} + uses: medyagh/setup-minikube@latest + + - name: Start minikube + if: ${{ matrix.auth-provider == 'kubernetes' }} + run: | + minikube start + kubectl get pods -A + + - name: Configure Kube Auth + if: ${{ matrix.auth-provider == 'kubernetes' }} + run: | + kubectl create namespace llama-stack + kubectl create serviceaccount llama-stack-auth -n llama-stack + kubectl create rolebinding llama-stack-auth-rolebinding --clusterrole=admin --serviceaccount=llama-stack:llama-stack-auth -n llama-stack + kubectl create token llama-stack-auth -n llama-stack > llama-stack-auth-token + + - name: Set Kubernetes Config + if: ${{ matrix.auth-provider == 'kubernetes' }} + run: | + echo "KUBERNETES_API_SERVER_URL=$(kubectl config view --minify -o jsonpath='{.clusters[0].cluster.server}')" >> $GITHUB_ENV + echo "KUBERNETES_CA_CERT_PATH=$(kubectl config view --minify -o jsonpath='{.clusters[0].cluster.certificate-authority}')" >> $GITHUB_ENV + + - name: Set Kube Auth Config and run server + env: + INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct" + if: ${{ matrix.auth-provider == 'kubernetes' }} + run: | + run_dir=$(mktemp -d) + cat <<'EOF' > $run_dir/run.yaml + version: '2' + image_name: kube + apis: + - agents + - datasetio + - eval + - inference + - safety + - scoring + - telemetry + - tool_runtime + - vector_io + providers: + agents: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + persistence_store: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/agents_store.db + telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/ollama/trace_store.db} + server: + port: 8321 + EOF + yq eval '.server.auth = {"provider_type": "${{ matrix.auth-provider }}"}' -i $run_dir/run.yaml + yq eval '.server.auth.config = {"api_server_url": "${{ env.KUBERNETES_API_SERVER_URL }}", "ca_cert_path": "${{ env.KUBERNETES_CA_CERT_PATH }}"}' -i $run_dir/run.yaml + cat $run_dir/run.yaml + + source .venv/bin/activate + nohup uv run llama stack run $run_dir/run.yaml --image-type venv > server.log 2>&1 & + + - name: Wait for Llama Stack server to be ready + run: | + echo "Waiting for Llama Stack server..." + for i in {1..30}; do + if curl -s -L -H "Authorization: Bearer $(cat llama-stack-auth-token)" http://localhost:8321/v1/health | grep -q "OK"; then + echo "Llama Stack server is up!" + if grep -q "Enabling authentication with provider: ${{ matrix.auth-provider }}" server.log; then + echo "Llama Stack server is configured to use ${{ matrix.auth-provider }} auth" + exit 0 + else + echo "Llama Stack server is not configured to use ${{ matrix.auth-provider }} auth" + cat server.log + exit 1 + fi + fi + sleep 1 + done + echo "Llama Stack server failed to start" + cat server.log + exit 1 + + - name: Test auth + run: | + curl -s -L -H "Authorization: Bearer $(cat llama-stack-auth-token)" http://127.0.0.1:8321/v1/providers|jq diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 0eb252695..044569139 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -6,7 +6,6 @@ on: pull_request: branches: [ main ] paths: - - 'distributions/**' - 'llama_stack/**' - 'tests/integration/**' - 'uv.lock' @@ -34,19 +33,24 @@ jobs: uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Install uv - uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1 + uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 with: python-version: "3.10" + activate-environment: true - name: Install and start Ollama run: | # the ollama installer also starts the ollama service curl -fsSL https://ollama.com/install.sh | sh - - name: Pull Ollama image + # Do NOT cache models - pulling the cache is actually slower than just pulling the model. + # It takes ~45 seconds to pull the models from the cache and unpack it, but only 30 seconds to + # pull them directly. + # Maybe this is because the cache is being pulled at the same time by all the matrix jobs? + - name: Pull Ollama models (instruct and embed) run: | - # TODO: cache the model. OLLAMA_MODELS defaults to ~ollama/.ollama/models. ollama pull llama3.2:3b-instruct-fp16 + ollama pull all-minilm:latest - name: Set Up Environment and Install Dependencies run: | @@ -106,3 +110,16 @@ jobs: -k "not(builtin_tool or safety_with_image or code_interpreter or test_rag)" \ --text-model="meta-llama/Llama-3.2-3B-Instruct" \ --embedding-model=all-MiniLM-L6-v2 + + - name: Write ollama logs to file + run: | + sudo journalctl -u ollama.service > ollama.log + + - name: Upload all logs to artifacts + if: always() + uses: actions/upload-artifact@v4 + with: + name: logs-${{ github.run_id }}-${{ github.run_attempt }}-${{ matrix.client-type }}-${{ matrix.test-type }} + path: | + *.log + retention-days: 1 diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 17a42dd26..4df04fbb0 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -18,7 +18,7 @@ jobs: uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python - uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: python-version: '3.11' cache: pip @@ -27,6 +27,8 @@ jobs: .pre-commit-config.yaml - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 + env: + SKIP: no-commit-to-branch - name: Verify if there are any diff files after pre-commit run: | diff --git a/.github/workflows/providers-build.yml b/.github/workflows/providers-build.yml index 010894283..7c136907c 100644 --- a/.github/workflows/providers-build.yml +++ b/.github/workflows/providers-build.yml @@ -51,12 +51,12 @@ jobs: uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python - uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: python-version: '3.10' - name: Install uv - uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1 + uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 with: python-version: "3.10" @@ -81,3 +81,120 @@ jobs: run: | source test/bin/activate uv pip list + + build-single-provider: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: '3.10' + + - name: Install uv + uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 + with: + python-version: "3.10" + + - name: Install LlamaStack + run: | + uv venv + source .venv/bin/activate + uv pip install -e . + + - name: Build a single provider + run: | + USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --image-type venv --image-name test --providers inference=remote::ollama + + build-custom-container-distribution: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: '3.10' + + - name: Install uv + uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 + with: + python-version: "3.10" + + - name: Install LlamaStack + run: | + uv venv + source .venv/bin/activate + uv pip install -e . + + - name: Build a single provider + run: | + yq -i '.image_type = "container"' llama_stack/templates/dev/build.yaml + yq -i '.image_name = "test"' llama_stack/templates/dev/build.yaml + USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --config llama_stack/templates/dev/build.yaml + + - name: Inspect the container image entrypoint + run: | + IMAGE_ID=$(docker images --format "{{.Repository}}:{{.Tag}}" | head -n 1) + entrypoint=$(docker inspect --format '{{ .Config.Entrypoint }}' $IMAGE_ID) + echo "Entrypoint: $entrypoint" + if [ "$entrypoint" != "[python -m llama_stack.distribution.server.server --config /app/run.yaml]" ]; then + echo "Entrypoint is not correct" + exit 1 + fi + + build-ubi9-container-distribution: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: '3.10' + + - name: Install uv + uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1 + with: + python-version: "3.10" + + - name: Install LlamaStack + run: | + uv venv + source .venv/bin/activate + uv pip install -e . + + - name: Pin template to UBI9 base + run: | + yq -i ' + .image_type = "container" | + .image_name = "ubi9-test" | + .distribution_spec.container_image = "registry.access.redhat.com/ubi9:latest" + ' llama_stack/templates/dev/build.yaml + + - name: Build dev container (UBI9) + env: + USE_COPY_NOT_MOUNT: "true" + LLAMA_STACK_DIR: "." + run: | + uv run llama stack build --config llama_stack/templates/dev/build.yaml + + - name: Inspect UBI9 image + run: | + IMAGE_ID=$(docker images --format "{{.Repository}}:{{.Tag}}" | head -n 1) + entrypoint=$(docker inspect --format '{{ .Config.Entrypoint }}' $IMAGE_ID) + echo "Entrypoint: $entrypoint" + if [ "$entrypoint" != "[python -m llama_stack.distribution.server.server --config /app/run.yaml]" ]; then + echo "Entrypoint is not correct" + exit 1 + fi + + echo "Checking /etc/os-release in $IMAGE_ID" + docker run --rm --entrypoint sh "$IMAGE_ID" -c \ + 'source /etc/os-release && echo "$ID"' \ + | grep -qE '^(rhel|ubi)$' \ + || { echo "Base image is not UBI 9!"; exit 1; } diff --git a/.github/workflows/test-external-providers.yml b/.github/workflows/test-external-providers.yml index 2ead8f845..b5b9645c1 100644 --- a/.github/workflows/test-external-providers.yml +++ b/.github/workflows/test-external-providers.yml @@ -5,89 +5,74 @@ on: branches: [ main ] pull_request: branches: [ main ] + paths: + - 'llama_stack/**' + - 'tests/integration/**' + - 'uv.lock' + - 'pyproject.toml' + - 'requirements.txt' + - '.github/workflows/test-external-providers.yml' # This workflow jobs: test-external-providers: runs-on: ubuntu-latest + strategy: + matrix: + image-type: [venv] + # We don't do container yet, it's tricky to install a package from the host into the + # container and point 'uv pip install' to the correct path... steps: - name: Checkout repository uses: actions/checkout@v4 - name: Install uv - uses: astral-sh/setup-uv@v5 + uses: astral-sh/setup-uv@v6 with: python-version: "3.10" - - name: Install Ollama - run: | - curl -fsSL https://ollama.com/install.sh | sh - - - name: Pull Ollama image - run: | - ollama pull llama3.2:3b-instruct-fp16 - - - name: Start Ollama in background - run: | - nohup ollama run llama3.2:3b-instruct-fp16 --keepalive=30m > ollama.log 2>&1 & - - name: Set Up Environment and Install Dependencies run: | uv sync --extra dev --extra test uv pip install -e . - - name: Install Ollama custom provider + - name: Apply image type to config file + run: | + yq -i '.image_type = "${{ matrix.image-type }}"' tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml + cat tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml + + - name: Setup directory for Ollama custom provider run: | mkdir -p tests/external-provider/llama-stack-provider-ollama/src/ cp -a llama_stack/providers/remote/inference/ollama/ tests/external-provider/llama-stack-provider-ollama/src/llama_stack_provider_ollama - uv pip install tests/external-provider/llama-stack-provider-ollama - name: Create provider configuration run: | mkdir -p /tmp/providers.d/remote/inference cp tests/external-provider/llama-stack-provider-ollama/custom_ollama.yaml /tmp/providers.d/remote/inference/custom_ollama.yaml - - name: Wait for Ollama to start + - name: Build distro from config file run: | - echo "Waiting for Ollama..." - for i in {1..30}; do - if curl -s http://localhost:11434 | grep -q "Ollama is running"; then - echo "Ollama is running!" - exit 0 - fi - sleep 1 - done - echo "Ollama failed to start" - ollama ps - ollama.log - exit 1 + USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --config tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml - name: Start Llama Stack server in background + if: ${{ matrix.image-type }} == 'venv' env: INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct" run: | - source .venv/bin/activate - nohup uv run llama stack run tests/external-provider/llama-stack-provider-ollama/run.yaml --image-type venv > server.log 2>&1 & + source ci-test/bin/activate + uv run pip list + nohup uv run --active llama stack run tests/external-provider/llama-stack-provider-ollama/run.yaml --image-type ${{ matrix.image-type }} > server.log 2>&1 & - name: Wait for Llama Stack server to be ready run: | - echo "Waiting for Llama Stack server..." for i in {1..30}; do - if curl -s http://localhost:8321/v1/health | grep -q "OK"; then - echo "Llama Stack server is up!" - if grep -q "remote::custom_ollama from /tmp/providers.d/remote/inference/custom_ollama.yaml" server.log; then - echo "Llama Stack server is using custom Ollama provider" - exit 0 - else - echo "Llama Stack server is not using custom Ollama provider" - exit 1 - fi + if ! grep -q "remote::custom_ollama from /tmp/providers.d/remote/inference/custom_ollama.yaml" server.log; then + echo "Waiting for Llama Stack server to load the provider..." + sleep 1 + else + echo "Provider loaded" + exit 0 fi - sleep 1 done - echo "Llama Stack server failed to start" - cat server.log + echo "Provider failed to load" exit 1 - - - name: run inference tests - run: | - uv run pytest -v tests/integration/inference/test_text_inference.py --stack-config="http://localhost:8321" --text-model="meta-llama/Llama-3.2-3B-Instruct" --embedding-model=all-MiniLM-L6-v2 diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index 4b0c58b99..64a5bba37 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -6,7 +6,6 @@ on: pull_request: branches: [ main ] paths: - - 'distributions/**' - 'llama_stack/**' - 'tests/unit/**' - 'uv.lock' @@ -34,11 +33,11 @@ jobs: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python ${{ matrix.python }} - uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: python-version: ${{ matrix.python }} - - uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1 + - uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 with: python-version: ${{ matrix.python }} enable-cache: false diff --git a/.github/workflows/update-readthedocs.yml b/.github/workflows/update-readthedocs.yml index 794a727be..21e3b633d 100644 --- a/.github/workflows/update-readthedocs.yml +++ b/.github/workflows/update-readthedocs.yml @@ -36,12 +36,12 @@ jobs: uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python - uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: python-version: '3.11' - name: Install the latest version of uv - uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1 + uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 - name: Sync with uv run: uv sync --extra docs diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ff3bc1250..42228d828 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,6 +15,18 @@ repos: args: ['--maxkb=1000'] - id: end-of-file-fixer exclude: '^(.*\.svg)$' + - id: no-commit-to-branch + - id: check-yaml + args: ["--unsafe"] + - id: detect-private-key + - id: requirements-txt-fixer + - id: mixed-line-ending + args: [--fix=lf] # Forces to replace line ending by LF (line feed) + - id: check-executables-have-shebangs + - id: check-json + - id: check-shebang-scripts-are-executable + - id: check-symlinks + - id: check-toml - repo: https://github.com/Lucas-C/pre-commit-hooks rev: v1.5.4 diff --git a/CHANGELOG.md b/CHANGELOG.md index 5086094ad..c57f3e253 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,33 @@ # Changelog +# v0.2.3 +Published on: 2025-04-25T22:46:21Z + +## Highlights + +* OpenAI compatible inference endpoints and client-SDK support. `client.chat.completions.create()` now works. +* significant improvements and functionality added to the nVIDIA distribution +* many improvements to the test verification suite. +* new inference providers: Ramalama, IBM WatsonX +* many improvements to the Playground UI + + +--- + +# v0.2.2 +Published on: 2025-04-13T01:19:49Z + +## Main changes + +- Bring Your Own Provider (@leseb) - use out-of-tree provider code to execute the distribution server +- OpenAI compatible inference API in progress (@bbrowning) +- Provider verifications (@ehhuang) +- Many updates and fixes to playground +- Several llama4 related fixes + + +--- + # v0.2.1 Published on: 2025-04-05T23:13:00Z @@ -10,10 +38,10 @@ Published on: 2025-04-05T23:13:00Z # v0.2.0 Published on: 2025-04-05T19:04:29Z -## Llama 4 Support - -Checkout more at https://www.llama.com - +## Llama 4 Support + +Checkout more at https://www.llama.com + --- @@ -21,58 +49,58 @@ Checkout more at https://www.llama.com # v0.1.9 Published on: 2025-03-29T00:52:23Z -### Build and Test Agents -* Agents: Entire document context with attachments -* RAG: Documentation with sqlite-vec faiss comparison -* Getting started: Fixes to getting started notebook. - -### Agent Evals and Model Customization -* (**New**) Post-training: Add nemo customizer - -### Better Engineering -* Moved sqlite-vec to non-blocking calls -* Don't return a payload on file delete - - +### Build and Test Agents +* Agents: Entire document context with attachments +* RAG: Documentation with sqlite-vec faiss comparison +* Getting started: Fixes to getting started notebook. + +### Agent Evals and Model Customization +* (**New**) Post-training: Add nemo customizer + +### Better Engineering +* Moved sqlite-vec to non-blocking calls +* Don't return a payload on file delete + + --- # v0.1.8 Published on: 2025-03-24T01:28:50Z -# v0.1.8 Release Notes - -### Build and Test Agents -* Safety: Integrated NVIDIA as a safety provider. -* VectorDB: Added Qdrant as an inline provider. -* Agents: Added support for multiple tool groups in agents. -* Agents: Simplified imports for Agents in client package - - -### Agent Evals and Model Customization -* Introduced DocVQA and IfEval benchmarks. - -### Deploying and Monitoring Agents -* Introduced a Containerfile and image workflow for the Playground. -* Implemented support for Bearer (API Key) authentication. -* Added attribute-based access control for resources. -* Fixes on docker deployments: use --pull always and standardized the default port to 8321 -* Deprecated: /v1/inspect/providers use /v1/providers/ instead - -### Better Engineering -* Consolidated scripts under the ./scripts directory. -* Addressed mypy violations in various modules. -* Added Dependabot scans for Python dependencies. -* Implemented a scheduled workflow to update the changelog automatically. -* Enforced concurrency to reduce CI loads. - - -### New Contributors -* @cmodi-meta made their first contribution in https://github.com/meta-llama/llama-stack/pull/1650 -* @jeffmaury made their first contribution in https://github.com/meta-llama/llama-stack/pull/1671 -* @derekhiggins made their first contribution in https://github.com/meta-llama/llama-stack/pull/1698 -* @Bobbins228 made their first contribution in https://github.com/meta-llama/llama-stack/pull/1745 - +# v0.1.8 Release Notes + +### Build and Test Agents +* Safety: Integrated NVIDIA as a safety provider. +* VectorDB: Added Qdrant as an inline provider. +* Agents: Added support for multiple tool groups in agents. +* Agents: Simplified imports for Agents in client package + + +### Agent Evals and Model Customization +* Introduced DocVQA and IfEval benchmarks. + +### Deploying and Monitoring Agents +* Introduced a Containerfile and image workflow for the Playground. +* Implemented support for Bearer (API Key) authentication. +* Added attribute-based access control for resources. +* Fixes on docker deployments: use --pull always and standardized the default port to 8321 +* Deprecated: /v1/inspect/providers use /v1/providers/ instead + +### Better Engineering +* Consolidated scripts under the ./scripts directory. +* Addressed mypy violations in various modules. +* Added Dependabot scans for Python dependencies. +* Implemented a scheduled workflow to update the changelog automatically. +* Enforced concurrency to reduce CI loads. + + +### New Contributors +* @cmodi-meta made their first contribution in https://github.com/meta-llama/llama-stack/pull/1650 +* @jeffmaury made their first contribution in https://github.com/meta-llama/llama-stack/pull/1671 +* @derekhiggins made their first contribution in https://github.com/meta-llama/llama-stack/pull/1698 +* @Bobbins228 made their first contribution in https://github.com/meta-llama/llama-stack/pull/1745 + **Full Changelog**: https://github.com/meta-llama/llama-stack/compare/v0.1.7...v0.1.8 --- @@ -80,73 +108,73 @@ Published on: 2025-03-24T01:28:50Z # v0.1.7 Published on: 2025-03-14T22:30:51Z -## 0.1.7 Release Notes - -### Build and Test Agents -* Inference: ImageType is now refactored to LlamaStackImageType -* Inference: Added tests to measure TTFT -* Inference: Bring back usage metrics -* Agents: Added endpoint for get agent, list agents and list sessions -* Agents: Automated conversion of type hints in client tool for lite llm format -* Agents: Deprecated ToolResponseMessage in agent.resume API -* Added Provider API for listing and inspecting provider info - -### Agent Evals and Model Customization -* Eval: Added new eval benchmarks Math 500 and BFCL v3 -* Deploy and Monitoring of Agents -* Telemetry: Fix tracing to work across coroutines - -### Better Engineering -* Display code coverage for unit tests -* Updated call sites (inference, tool calls, agents) to move to async non blocking calls -* Unit tests also run on Python 3.11, 3.12, and 3.13 -* Added ollama inference to Integration tests CI -* Improved documentation across examples, testing, CLI, updated providers table ) - - - +## 0.1.7 Release Notes + +### Build and Test Agents +* Inference: ImageType is now refactored to LlamaStackImageType +* Inference: Added tests to measure TTFT +* Inference: Bring back usage metrics +* Agents: Added endpoint for get agent, list agents and list sessions +* Agents: Automated conversion of type hints in client tool for lite llm format +* Agents: Deprecated ToolResponseMessage in agent.resume API +* Added Provider API for listing and inspecting provider info + +### Agent Evals and Model Customization +* Eval: Added new eval benchmarks Math 500 and BFCL v3 +* Deploy and Monitoring of Agents +* Telemetry: Fix tracing to work across coroutines + +### Better Engineering +* Display code coverage for unit tests +* Updated call sites (inference, tool calls, agents) to move to async non blocking calls +* Unit tests also run on Python 3.11, 3.12, and 3.13 +* Added ollama inference to Integration tests CI +* Improved documentation across examples, testing, CLI, updated providers table ) + + + --- # v0.1.6 Published on: 2025-03-08T04:35:08Z -## 0.1.6 Release Notes - -### Build and Test Agents -* Inference: Fixed support for inline vllm provider -* (**New**) Agent: Build & Monitor Agent Workflows with Llama Stack + Anthropic's Best Practice [Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Agent_Workflows.ipynb) -* (**New**) Agent: Revamped agent [documentation](https://llama-stack.readthedocs.io/en/latest/building_applications/agent.html) with more details and examples -* Agent: Unify tools and Python SDK Agents API -* Agent: AsyncAgent Python SDK wrapper supporting async client tool calls -* Agent: Support python functions without @client_tool decorator as client tools -* Agent: deprecation for allow_resume_turn flag, and remove need to specify tool_prompt_format -* VectorIO: MilvusDB support added - -### Agent Evals and Model Customization -* (**New**) Agent: Llama Stack RAG Lifecycle [Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_RAG_Lifecycle.ipynb) -* Eval: Documentation for eval, scoring, adding new benchmarks -* Eval: Distribution template to run benchmarks on llama & non-llama models -* Eval: Ability to register new custom LLM-as-judge scoring functions -* (**New**) Looking for contributors for open benchmarks. See [documentation](https://llama-stack.readthedocs.io/en/latest/references/evals_reference/index.html#open-benchmark-contributing-guide) for details. - -### Deploy and Monitoring of Agents -* Better support for different log levels across all components for better monitoring - -### Better Engineering -* Enhance OpenAPI spec to include Error types across all APIs -* Moved all tests to /tests and created unit tests to run on each PR -* Removed all dependencies on llama-models repo - +## 0.1.6 Release Notes + +### Build and Test Agents +* Inference: Fixed support for inline vllm provider +* (**New**) Agent: Build & Monitor Agent Workflows with Llama Stack + Anthropic's Best Practice [Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Agent_Workflows.ipynb) +* (**New**) Agent: Revamped agent [documentation](https://llama-stack.readthedocs.io/en/latest/building_applications/agent.html) with more details and examples +* Agent: Unify tools and Python SDK Agents API +* Agent: AsyncAgent Python SDK wrapper supporting async client tool calls +* Agent: Support python functions without @client_tool decorator as client tools +* Agent: deprecation for allow_resume_turn flag, and remove need to specify tool_prompt_format +* VectorIO: MilvusDB support added + +### Agent Evals and Model Customization +* (**New**) Agent: Llama Stack RAG Lifecycle [Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_RAG_Lifecycle.ipynb) +* Eval: Documentation for eval, scoring, adding new benchmarks +* Eval: Distribution template to run benchmarks on llama & non-llama models +* Eval: Ability to register new custom LLM-as-judge scoring functions +* (**New**) Looking for contributors for open benchmarks. See [documentation](https://llama-stack.readthedocs.io/en/latest/references/evals_reference/index.html#open-benchmark-contributing-guide) for details. + +### Deploy and Monitoring of Agents +* Better support for different log levels across all components for better monitoring + +### Better Engineering +* Enhance OpenAPI spec to include Error types across all APIs +* Moved all tests to /tests and created unit tests to run on each PR +* Removed all dependencies on llama-models repo + --- # v0.1.5.1 Published on: 2025-02-28T22:37:44Z -## 0.1.5.1 Release Notes -* Fixes for security risk in https://github.com/meta-llama/llama-stack/pull/1327 and https://github.com/meta-llama/llama-stack/pull/1328 - +## 0.1.5.1 Release Notes +* Fixes for security risk in https://github.com/meta-llama/llama-stack/pull/1327 and https://github.com/meta-llama/llama-stack/pull/1328 + **Full Changelog**: https://github.com/meta-llama/llama-stack/compare/v0.1.5...v0.1.5.1 --- @@ -154,176 +182,176 @@ Published on: 2025-02-28T22:37:44Z # v0.1.5 Published on: 2025-02-28T18:14:01Z -## 0.1.5 Release Notes -### Build Agents -* Inference: Support more non-llama models (openai, anthropic, gemini) -* Inference: Can use the provider's model name in addition to the HF alias -* Inference: Fixed issues with calling tools that weren't specified in the prompt -* RAG: Improved system prompt for RAG and no more need for hard-coded rag-tool calling -* Embeddings: Added support for Nemo retriever embedding models -* Tools: Added support for MCP tools in Ollama Distribution -* Distributions: Added new Groq distribution - -### Customize Models -* Save post-trained checkpoint in SafeTensor format to allow Ollama inference provider to use the post-trained model - -### Monitor agents -* More comprehensive logging of agent steps including client tools -* Telemetry inputs/outputs are now structured and queryable -* Ability to retrieve agents session, turn, step by ids - -### Better Engineering -* Moved executorch Swift code out of this repo into the llama-stack-client-swift repo, similar to kotlin -* Move most logging to use logger instead of prints -* Completed text /chat-completion and /completion tests - +## 0.1.5 Release Notes +### Build Agents +* Inference: Support more non-llama models (openai, anthropic, gemini) +* Inference: Can use the provider's model name in addition to the HF alias +* Inference: Fixed issues with calling tools that weren't specified in the prompt +* RAG: Improved system prompt for RAG and no more need for hard-coded rag-tool calling +* Embeddings: Added support for Nemo retriever embedding models +* Tools: Added support for MCP tools in Ollama Distribution +* Distributions: Added new Groq distribution + +### Customize Models +* Save post-trained checkpoint in SafeTensor format to allow Ollama inference provider to use the post-trained model + +### Monitor agents +* More comprehensive logging of agent steps including client tools +* Telemetry inputs/outputs are now structured and queryable +* Ability to retrieve agents session, turn, step by ids + +### Better Engineering +* Moved executorch Swift code out of this repo into the llama-stack-client-swift repo, similar to kotlin +* Move most logging to use logger instead of prints +* Completed text /chat-completion and /completion tests + --- # v0.1.4 Published on: 2025-02-25T00:02:43Z -## v0.1.4 Release Notes -Here are the key changes coming as part of this release: - -### Build and Test Agents -* Inference: Added support for non-llama models -* Inference: Added option to list all downloaded models and remove models -* Agent: Introduce new api agents.resume_turn to include client side tool execution in the same turn -* Agent: AgentConfig introduces new variable “tool_config” that allows for better tool configuration and system prompt overrides -* Agent: Added logging for agent step start and completion times -* Agent: Added support for logging for tool execution metadata -* Embedding: Updated /inference/embeddings to support asymmetric models, truncation and variable sized outputs -* Embedding: Updated embedding models for Ollama, Together, and Fireworks with available defaults -* VectorIO: Improved performance of sqlite-vec using chunked writes -### Agent Evals and Model Customization -* Deprecated api /eval-tasks. Use /eval/benchmark instead -* Added CPU training support for TorchTune -### Deploy and Monitoring of Agents -* Consistent view of client and server tool calls in telemetry -### Better Engineering -* Made tests more data-driven for consistent evaluation -* Fixed documentation links and improved API reference generation -* Various small fixes for build scripts and system reliability - - +## v0.1.4 Release Notes +Here are the key changes coming as part of this release: + +### Build and Test Agents +* Inference: Added support for non-llama models +* Inference: Added option to list all downloaded models and remove models +* Agent: Introduce new api agents.resume_turn to include client side tool execution in the same turn +* Agent: AgentConfig introduces new variable “tool_config” that allows for better tool configuration and system prompt overrides +* Agent: Added logging for agent step start and completion times +* Agent: Added support for logging for tool execution metadata +* Embedding: Updated /inference/embeddings to support asymmetric models, truncation and variable sized outputs +* Embedding: Updated embedding models for Ollama, Together, and Fireworks with available defaults +* VectorIO: Improved performance of sqlite-vec using chunked writes +### Agent Evals and Model Customization +* Deprecated api /eval-tasks. Use /eval/benchmark instead +* Added CPU training support for TorchTune +### Deploy and Monitoring of Agents +* Consistent view of client and server tool calls in telemetry +### Better Engineering +* Made tests more data-driven for consistent evaluation +* Fixed documentation links and improved API reference generation +* Various small fixes for build scripts and system reliability + + --- # v0.1.3 Published on: 2025-02-14T20:24:32Z -## v0.1.3 Release - -Here are some key changes that are coming as part of this release. - -### Build and Test Agents -Streamlined the initial development experience -- Added support for llama stack run --image-type venv -- Enhanced vector store options with new sqlite-vec provider and improved Qdrant integration -- vLLM improvements for tool calling and logprobs -- Better handling of sporadic code_interpreter tool calls - -### Agent Evals -Better benchmarking and Agent performance assessment -- Renamed eval API /eval-task to /benchmarks -- Improved documentation and notebooks for RAG and evals - -### Deploy and Monitoring of Agents -Improved production readiness -- Added usage metrics collection for chat completions -- CLI improvements for provider information -- Improved error handling and system reliability -- Better model endpoint handling and accessibility -- Improved signal handling on distro server - -### Better Engineering -Infrastructure and code quality improvements -- Faster text-based chat completion tests -- Improved testing for non-streaming agent apis -- Standardized import formatting with ruff linter -- Added conventional commits standard -- Fixed documentation parsing issues - +## v0.1.3 Release + +Here are some key changes that are coming as part of this release. + +### Build and Test Agents +Streamlined the initial development experience +- Added support for llama stack run --image-type venv +- Enhanced vector store options with new sqlite-vec provider and improved Qdrant integration +- vLLM improvements for tool calling and logprobs +- Better handling of sporadic code_interpreter tool calls + +### Agent Evals +Better benchmarking and Agent performance assessment +- Renamed eval API /eval-task to /benchmarks +- Improved documentation and notebooks for RAG and evals + +### Deploy and Monitoring of Agents +Improved production readiness +- Added usage metrics collection for chat completions +- CLI improvements for provider information +- Improved error handling and system reliability +- Better model endpoint handling and accessibility +- Improved signal handling on distro server + +### Better Engineering +Infrastructure and code quality improvements +- Faster text-based chat completion tests +- Improved testing for non-streaming agent apis +- Standardized import formatting with ruff linter +- Added conventional commits standard +- Fixed documentation parsing issues + --- # v0.1.2 Published on: 2025-02-07T22:06:49Z -# TL;DR -- Several stabilizations to development flows after the switch to `uv` -- Migrated CI workflows to new OSS repo - [llama-stack-ops](https://github.com/meta-llama/llama-stack-ops) -- Added automated rebuilds for ReadTheDocs -- Llama Stack server supports HTTPS -- Added system prompt overrides support -- Several bug fixes and improvements to documentation (check out Kubernetes deployment guide by @terrytangyuan ) - +# TL;DR +- Several stabilizations to development flows after the switch to `uv` +- Migrated CI workflows to new OSS repo - [llama-stack-ops](https://github.com/meta-llama/llama-stack-ops) +- Added automated rebuilds for ReadTheDocs +- Llama Stack server supports HTTPS +- Added system prompt overrides support +- Several bug fixes and improvements to documentation (check out Kubernetes deployment guide by @terrytangyuan ) + --- # v0.1.1 Published on: 2025-02-02T02:29:24Z -A bunch of small / big improvements everywhere including support for Windows, switching to `uv` and many provider improvements. - +A bunch of small / big improvements everywhere including support for Windows, switching to `uv` and many provider improvements. + --- # v0.1.0 Published on: 2025-01-24T17:47:47Z -We are excited to announce a stable API release of Llama Stack, which enables developers to build RAG applications and Agents using tools and safety shields, monitor and those agents with telemetry, and evaluate the agent with scoring functions. - -## Context -GenAI application developers need more than just an LLM - they need to integrate tools, connect with their data sources, establish guardrails, and ground the LLM responses effectively. Currently, developers must piece together various tools and APIs, complicating the development lifecycle and increasing costs. The result is that developers are spending more time on these integrations rather than focusing on the application logic itself. The bespoke coupling of components also makes it challenging to adopt state-of-the-art solutions in the rapidly evolving GenAI space. This is particularly difficult for open models like Llama, as best practices are not widely established in the open. - -Llama Stack was created to provide developers with a comprehensive and coherent interface that simplifies AI application development and codifies best practices across the Llama ecosystem. Since our launch in September 2024, we have seen a huge uptick in interest in Llama Stack APIs by both AI developers and from partners building AI services with Llama models. Partners like Nvidia, Fireworks, and Ollama have collaborated with us to develop implementations across various APIs, including inference, memory, and safety. - -With Llama Stack, you can easily build a RAG agent which can also search the web, do complex math, and custom tool calling. You can use telemetry to inspect those traces, and convert telemetry into evals datasets. And with Llama Stack’s plugin architecture and prepackage distributions, you choose to run your agent anywhere - in the cloud with our partners, deploy your own environment using virtualenv, conda, or Docker, operate locally with Ollama, or even run on mobile devices with our SDKs. Llama Stack offers unprecedented flexibility while also simplifying the developer experience. - -## Release -After iterating on the APIs for the last 3 months, today we’re launching a stable release (V1) of the Llama Stack APIs and the corresponding llama-stack server and client packages(v0.1.0). We now have automated tests for providers. These tests make sure that all provider implementations are verified. Developers can now easily and reliably select distributions or providers based on their specific requirements. - -There are example standalone apps in llama-stack-apps. - - -## Key Features of this release - -- **Unified API Layer** - - Inference: Run LLM models - - RAG: Store and retrieve knowledge for RAG - - Agents: Build multi-step agentic workflows - - Tools: Register tools that can be called by the agent - - Safety: Apply content filtering and safety policies - - Evaluation: Test model and agent quality - - Telemetry: Collect and analyze usage data and complex agentic traces - - Post Training ( Coming Soon ): Fine tune models for specific use cases - -- **Rich Provider Ecosystem** - - Local Development: Meta's Reference, Ollama - - Cloud: Fireworks, Together, Nvidia, AWS Bedrock, Groq, Cerebras - - On-premises: Nvidia NIM, vLLM, TGI, Dell-TGI - - On-device: iOS and Android support - -- **Built for Production** - - Pre-packaged distributions for common deployment scenarios - - Backwards compatibility across model versions - - Comprehensive evaluation capabilities - - Full observability and monitoring - -- **Multiple developer interfaces** - - CLI: Command line interface - - Python SDK - - Swift iOS SDK - - Kotlin Android SDK - -- **Sample llama stack applications** - - Python - - iOS - - Android - - +We are excited to announce a stable API release of Llama Stack, which enables developers to build RAG applications and Agents using tools and safety shields, monitor and those agents with telemetry, and evaluate the agent with scoring functions. + +## Context +GenAI application developers need more than just an LLM - they need to integrate tools, connect with their data sources, establish guardrails, and ground the LLM responses effectively. Currently, developers must piece together various tools and APIs, complicating the development lifecycle and increasing costs. The result is that developers are spending more time on these integrations rather than focusing on the application logic itself. The bespoke coupling of components also makes it challenging to adopt state-of-the-art solutions in the rapidly evolving GenAI space. This is particularly difficult for open models like Llama, as best practices are not widely established in the open. + +Llama Stack was created to provide developers with a comprehensive and coherent interface that simplifies AI application development and codifies best practices across the Llama ecosystem. Since our launch in September 2024, we have seen a huge uptick in interest in Llama Stack APIs by both AI developers and from partners building AI services with Llama models. Partners like Nvidia, Fireworks, and Ollama have collaborated with us to develop implementations across various APIs, including inference, memory, and safety. + +With Llama Stack, you can easily build a RAG agent which can also search the web, do complex math, and custom tool calling. You can use telemetry to inspect those traces, and convert telemetry into evals datasets. And with Llama Stack’s plugin architecture and prepackage distributions, you choose to run your agent anywhere - in the cloud with our partners, deploy your own environment using virtualenv, conda, or Docker, operate locally with Ollama, or even run on mobile devices with our SDKs. Llama Stack offers unprecedented flexibility while also simplifying the developer experience. + +## Release +After iterating on the APIs for the last 3 months, today we’re launching a stable release (V1) of the Llama Stack APIs and the corresponding llama-stack server and client packages(v0.1.0). We now have automated tests for providers. These tests make sure that all provider implementations are verified. Developers can now easily and reliably select distributions or providers based on their specific requirements. + +There are example standalone apps in llama-stack-apps. + + +## Key Features of this release + +- **Unified API Layer** + - Inference: Run LLM models + - RAG: Store and retrieve knowledge for RAG + - Agents: Build multi-step agentic workflows + - Tools: Register tools that can be called by the agent + - Safety: Apply content filtering and safety policies + - Evaluation: Test model and agent quality + - Telemetry: Collect and analyze usage data and complex agentic traces + - Post Training ( Coming Soon ): Fine tune models for specific use cases + +- **Rich Provider Ecosystem** + - Local Development: Meta's Reference, Ollama + - Cloud: Fireworks, Together, Nvidia, AWS Bedrock, Groq, Cerebras + - On-premises: Nvidia NIM, vLLM, TGI, Dell-TGI + - On-device: iOS and Android support + +- **Built for Production** + - Pre-packaged distributions for common deployment scenarios + - Backwards compatibility across model versions + - Comprehensive evaluation capabilities + - Full observability and monitoring + +- **Multiple developer interfaces** + - CLI: Command line interface + - Python SDK + - Swift iOS SDK + - Kotlin Android SDK + +- **Sample llama stack applications** + - Python + - iOS + - Android + + --- @@ -337,8 +365,8 @@ Published on: 2025-01-22T22:24:01Z # v0.0.63 Published on: 2024-12-18T07:17:43Z -A small but important bug-fix release to update the URL datatype for the client-SDKs. The issue affected multimodal agentic turns especially. - +A small but important bug-fix release to update the URL datatype for the client-SDKs. The issue affected multimodal agentic turns especially. + **Full Changelog**: https://github.com/meta-llama/llama-stack/compare/v0.0.62...v0.0.63 --- @@ -374,39 +402,39 @@ Published on: 2024-11-22T00:36:09Z # v0.0.53 Published on: 2024-11-20T22:18:00Z -🚀 Initial Release Notes for Llama Stack! - -### Added -- Resource-oriented design for models, shields, memory banks, datasets and eval tasks -- Persistence for registered objects with distribution -- Ability to persist memory banks created for FAISS -- PostgreSQL KVStore implementation -- Environment variable placeholder support in run.yaml files -- Comprehensive Zero-to-Hero notebooks and quickstart guides -- Support for quantized models in Ollama -- Vision models support for Together, Fireworks, Meta-Reference, and Ollama, and vLLM -- Bedrock distribution with safety shields support -- Evals API with task registration and scoring functions -- MMLU and SimpleQA benchmark scoring functions -- Huggingface dataset provider integration for benchmarks -- Support for custom dataset registration from local paths -- Benchmark evaluation CLI tools with visualization tables -- RAG evaluation scoring functions and metrics -- Local persistence for datasets and eval tasks - -### Changed -- Split safety into distinct providers (llama-guard, prompt-guard, code-scanner) -- Changed provider naming convention (`impls` → `inline`, `adapters` → `remote`) -- Updated API signatures for dataset and eval task registration -- Restructured folder organization for providers -- Enhanced Docker build configuration -- Added version prefixing for REST API routes -- Enhanced evaluation task registration workflow -- Improved benchmark evaluation output formatting -- Restructured evals folder organization for better modularity - -### Removed -- `llama stack configure` command - +🚀 Initial Release Notes for Llama Stack! + +### Added +- Resource-oriented design for models, shields, memory banks, datasets and eval tasks +- Persistence for registered objects with distribution +- Ability to persist memory banks created for FAISS +- PostgreSQL KVStore implementation +- Environment variable placeholder support in run.yaml files +- Comprehensive Zero-to-Hero notebooks and quickstart guides +- Support for quantized models in Ollama +- Vision models support for Together, Fireworks, Meta-Reference, and Ollama, and vLLM +- Bedrock distribution with safety shields support +- Evals API with task registration and scoring functions +- MMLU and SimpleQA benchmark scoring functions +- Huggingface dataset provider integration for benchmarks +- Support for custom dataset registration from local paths +- Benchmark evaluation CLI tools with visualization tables +- RAG evaluation scoring functions and metrics +- Local persistence for datasets and eval tasks + +### Changed +- Split safety into distinct providers (llama-guard, prompt-guard, code-scanner) +- Changed provider naming convention (`impls` → `inline`, `adapters` → `remote`) +- Updated API signatures for dataset and eval task registration +- Restructured folder organization for providers +- Enhanced Docker build configuration +- Added version prefixing for REST API routes +- Enhanced evaluation task registration workflow +- Improved benchmark evaluation output formatting +- Restructured evals folder organization for better modularity + +### Removed +- `llama stack configure` command + --- diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5828250d0..17eed43c5 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -141,11 +141,18 @@ uv sync ## Coding Style -* Comments should provide meaningful insights into the code. Avoid filler comments that simply describe the next step, as they create unnecessary clutter, same goes for docstrings. -* Prefer comments to clarify surprising behavior and/or relationships between parts of the code rather than explain what the next line of code does. -* Catching exceptions, prefer using a specific exception type rather than a broad catch-all like `Exception`. +* Comments should provide meaningful insights into the code. Avoid filler comments that simply + describe the next step, as they create unnecessary clutter, same goes for docstrings. +* Prefer comments to clarify surprising behavior and/or relationships between parts of the code + rather than explain what the next line of code does. +* Catching exceptions, prefer using a specific exception type rather than a broad catch-all like + `Exception`. * Error messages should be prefixed with "Failed to ..." -* 4 spaces for indentation rather than tabs +* 4 spaces for indentation rather than tab +* When using `# noqa` to suppress a style or linter warning, include a comment explaining the + justification for bypassing the check. +* When using `# type: ignore` to suppress a mypy warning, include a comment explaining the + justification for bypassing the check. ## Common Tasks diff --git a/README.md b/README.md index 8c201e43d..b2b2d12d9 100644 --- a/README.md +++ b/README.md @@ -70,6 +70,13 @@ As more providers start supporting Llama 4, you can use them in Llama Stack as w +### 🚀 One-Line Installer 🚀 + +To try Llama Stack locally, run: + +```bash +curl -LsSf https://github.com/meta-llama/llama-stack/raw/main/install.sh | sh +``` ### Overview @@ -119,6 +126,7 @@ Here is a list of the various API providers and available distributions that can | OpenAI | Hosted | | ✅ | | | | | Anthropic | Hosted | | ✅ | | | | | Gemini | Hosted | | ✅ | | | | +| watsonx | Hosted | | ✅ | | | | ### Distributions @@ -128,7 +136,6 @@ A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider | **Distribution** | **Llama Stack Docker** | Start This Distribution | |:---------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------:| | Meta Reference | [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-gpu.html) | -| Meta Reference Quantized | [llamastack/distribution-meta-reference-quantized-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-quantized-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-quantized-gpu.html) | | SambaNova | [llamastack/distribution-sambanova](https://hub.docker.com/repository/docker/llamastack/distribution-sambanova/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/sambanova.html) | | Cerebras | [llamastack/distribution-cerebras](https://hub.docker.com/repository/docker/llamastack/distribution-cerebras/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/cerebras.html) | | Ollama | [llamastack/distribution-ollama](https://hub.docker.com/repository/docker/llamastack/distribution-ollama/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/ollama.html) | diff --git a/docs/_static/css/my_theme.css b/docs/_static/css/my_theme.css index a587f866d..d078ec057 100644 --- a/docs/_static/css/my_theme.css +++ b/docs/_static/css/my_theme.css @@ -27,3 +27,9 @@ pre { white-space: pre-wrap !important; word-break: break-all; } + +[data-theme="dark"] .mermaid { + background-color: #f4f4f6 !important; + border-radius: 6px; + padding: 0.5em; + } diff --git a/docs/_static/js/detect_theme.js b/docs/_static/js/detect_theme.js index 484b2bb8b..712565ef7 100644 --- a/docs/_static/js/detect_theme.js +++ b/docs/_static/js/detect_theme.js @@ -1,9 +1,32 @@ document.addEventListener("DOMContentLoaded", function () { const prefersDark = window.matchMedia("(prefers-color-scheme: dark)").matches; const htmlElement = document.documentElement; - if (prefersDark) { - htmlElement.setAttribute("data-theme", "dark"); + + // Check if theme is saved in localStorage + const savedTheme = localStorage.getItem("sphinx-rtd-theme"); + + if (savedTheme) { + // Use the saved theme preference + htmlElement.setAttribute("data-theme", savedTheme); + document.body.classList.toggle("dark", savedTheme === "dark"); } else { - htmlElement.setAttribute("data-theme", "light"); + // Fall back to system preference + const theme = prefersDark ? "dark" : "light"; + htmlElement.setAttribute("data-theme", theme); + document.body.classList.toggle("dark", theme === "dark"); + // Save initial preference + localStorage.setItem("sphinx-rtd-theme", theme); } + + // Listen for theme changes from the existing toggle + const observer = new MutationObserver(function(mutations) { + mutations.forEach(function(mutation) { + if (mutation.attributeName === "data-theme") { + const currentTheme = htmlElement.getAttribute("data-theme"); + localStorage.setItem("sphinx-rtd-theme", currentTheme); + } + }); + }); + + observer.observe(htmlElement, { attributes: true }); }); diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html index 54d888441..2875f0f41 100644 --- a/docs/_static/llama-stack-spec.html +++ b/docs/_static/llama-stack-spec.html @@ -497,6 +497,54 @@ } } }, + "/v1/openai/v1/responses": { + "post": { + "responses": { + "200": { + "description": "Runtime representation of an annotated type.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenAIResponseObject" + } + }, + "text/event-stream": { + "schema": { + "$ref": "#/components/schemas/OpenAIResponseObjectStream" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Agents" + ], + "description": "Create a new OpenAI response.", + "parameters": [], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/CreateOpenaiResponseRequest" + } + } + }, + "required": true + } + } + }, "/v1/files": { "get": { "responses": { @@ -1278,6 +1326,49 @@ ] } }, + "/v1/openai/v1/responses/{id}": { + "get": { + "responses": { + "200": { + "description": "An OpenAIResponseObject.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenAIResponseObject" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Agents" + ], + "description": "Retrieve an OpenAI response by its ID.", + "parameters": [ + { + "name": "id", + "in": "path", + "description": "The ID of the OpenAI response to retrieve.", + "required": true, + "schema": { + "type": "string" + } + } + ] + } + }, "/v1/scoring-functions/{scoring_fn_id}": { "get": { "responses": { @@ -5221,17 +5312,25 @@ "default": 10 }, "model": { - "type": "string" + "type": "string", + "description": "The model identifier to use for the agent" }, "instructions": { - "type": "string" + "type": "string", + "description": "The system instructions for the agent" + }, + "name": { + "type": "string", + "description": "Optional name for the agent, used in telemetry and identification" }, "enable_session_persistence": { "type": "boolean", - "default": false + "default": false, + "description": "Optional flag indicating whether session data has to be persisted" }, "response_format": { - "$ref": "#/components/schemas/ResponseFormat" + "$ref": "#/components/schemas/ResponseFormat", + "description": "Optional response format configuration" } }, "additionalProperties": false, @@ -5239,7 +5338,8 @@ "model", "instructions" ], - "title": "AgentConfig" + "title": "AgentConfig", + "description": "Configuration for an agent." }, "AgentTool": { "oneOf": [ @@ -6183,6 +6283,430 @@ ], "title": "AgentTurnResponseTurnStartPayload" }, + "OpenAIResponseInputMessage": { + "type": "object", + "properties": { + "content": { + "oneOf": [ + { + "type": "string" + }, + { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIResponseInputMessageContent" + } + } + ] + }, + "role": { + "oneOf": [ + { + "type": "string", + "const": "system" + }, + { + "type": "string", + "const": "developer" + }, + { + "type": "string", + "const": "user" + }, + { + "type": "string", + "const": "assistant" + } + ] + }, + "type": { + "type": "string", + "const": "message", + "default": "message" + } + }, + "additionalProperties": false, + "required": [ + "content", + "role" + ], + "title": "OpenAIResponseInputMessage" + }, + "OpenAIResponseInputMessageContent": { + "oneOf": [ + { + "$ref": "#/components/schemas/OpenAIResponseInputMessageContentText" + }, + { + "$ref": "#/components/schemas/OpenAIResponseInputMessageContentImage" + } + ], + "discriminator": { + "propertyName": "type", + "mapping": { + "input_text": "#/components/schemas/OpenAIResponseInputMessageContentText", + "input_image": "#/components/schemas/OpenAIResponseInputMessageContentImage" + } + } + }, + "OpenAIResponseInputMessageContentImage": { + "type": "object", + "properties": { + "detail": { + "oneOf": [ + { + "type": "string", + "const": "low" + }, + { + "type": "string", + "const": "high" + }, + { + "type": "string", + "const": "auto" + } + ], + "default": "auto" + }, + "type": { + "type": "string", + "const": "input_image", + "default": "input_image" + }, + "image_url": { + "type": "string" + } + }, + "additionalProperties": false, + "required": [ + "detail", + "type" + ], + "title": "OpenAIResponseInputMessageContentImage" + }, + "OpenAIResponseInputMessageContentText": { + "type": "object", + "properties": { + "text": { + "type": "string" + }, + "type": { + "type": "string", + "const": "input_text", + "default": "input_text" + } + }, + "additionalProperties": false, + "required": [ + "text", + "type" + ], + "title": "OpenAIResponseInputMessageContentText" + }, + "OpenAIResponseInputTool": { + "type": "object", + "properties": { + "type": { + "oneOf": [ + { + "type": "string", + "const": "web_search" + }, + { + "type": "string", + "const": "web_search_preview_2025_03_11" + } + ], + "default": "web_search" + }, + "search_context_size": { + "type": "string", + "default": "medium" + } + }, + "additionalProperties": false, + "required": [ + "type" + ], + "title": "OpenAIResponseInputToolWebSearch" + }, + "CreateOpenaiResponseRequest": { + "type": "object", + "properties": { + "input": { + "oneOf": [ + { + "type": "string" + }, + { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIResponseInputMessage" + } + } + ], + "description": "Input message(s) to create the response." + }, + "model": { + "type": "string", + "description": "The underlying LLM used for completions." + }, + "previous_response_id": { + "type": "string", + "description": "(Optional) if specified, the new response will be a continuation of the previous response. This can be used to easily fork-off new responses from existing responses." + }, + "store": { + "type": "boolean" + }, + "stream": { + "type": "boolean" + }, + "temperature": { + "type": "number" + }, + "tools": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIResponseInputTool" + } + } + }, + "additionalProperties": false, + "required": [ + "input", + "model" + ], + "title": "CreateOpenaiResponseRequest" + }, + "OpenAIResponseError": { + "type": "object", + "properties": { + "code": { + "type": "string" + }, + "message": { + "type": "string" + } + }, + "additionalProperties": false, + "required": [ + "code", + "message" + ], + "title": "OpenAIResponseError" + }, + "OpenAIResponseObject": { + "type": "object", + "properties": { + "created_at": { + "type": "integer" + }, + "error": { + "$ref": "#/components/schemas/OpenAIResponseError" + }, + "id": { + "type": "string" + }, + "model": { + "type": "string" + }, + "object": { + "type": "string", + "const": "response", + "default": "response" + }, + "output": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIResponseOutput" + } + }, + "parallel_tool_calls": { + "type": "boolean", + "default": false + }, + "previous_response_id": { + "type": "string" + }, + "status": { + "type": "string" + }, + "temperature": { + "type": "number" + }, + "top_p": { + "type": "number" + }, + "truncation": { + "type": "string" + }, + "user": { + "type": "string" + } + }, + "additionalProperties": false, + "required": [ + "created_at", + "id", + "model", + "object", + "output", + "parallel_tool_calls", + "status" + ], + "title": "OpenAIResponseObject" + }, + "OpenAIResponseOutput": { + "oneOf": [ + { + "$ref": "#/components/schemas/OpenAIResponseOutputMessage" + }, + { + "$ref": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall" + } + ], + "discriminator": { + "propertyName": "type", + "mapping": { + "message": "#/components/schemas/OpenAIResponseOutputMessage", + "web_search_call": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall" + } + } + }, + "OpenAIResponseOutputMessage": { + "type": "object", + "properties": { + "id": { + "type": "string" + }, + "content": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIResponseOutputMessageContent" + } + }, + "role": { + "type": "string", + "const": "assistant", + "default": "assistant" + }, + "status": { + "type": "string" + }, + "type": { + "type": "string", + "const": "message", + "default": "message" + } + }, + "additionalProperties": false, + "required": [ + "id", + "content", + "role", + "status", + "type" + ], + "title": "OpenAIResponseOutputMessage" + }, + "OpenAIResponseOutputMessageContent": { + "type": "object", + "properties": { + "text": { + "type": "string" + }, + "type": { + "type": "string", + "const": "output_text", + "default": "output_text" + } + }, + "additionalProperties": false, + "required": [ + "text", + "type" + ], + "title": "OpenAIResponseOutputMessageContentOutputText" + }, + "OpenAIResponseOutputMessageWebSearchToolCall": { + "type": "object", + "properties": { + "id": { + "type": "string" + }, + "status": { + "type": "string" + }, + "type": { + "type": "string", + "const": "web_search_call", + "default": "web_search_call" + } + }, + "additionalProperties": false, + "required": [ + "id", + "status", + "type" + ], + "title": "OpenAIResponseOutputMessageWebSearchToolCall" + }, + "OpenAIResponseObjectStream": { + "oneOf": [ + { + "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCreated" + }, + { + "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted" + } + ], + "discriminator": { + "propertyName": "type", + "mapping": { + "response.created": "#/components/schemas/OpenAIResponseObjectStreamResponseCreated", + "response.completed": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted" + } + } + }, + "OpenAIResponseObjectStreamResponseCompleted": { + "type": "object", + "properties": { + "response": { + "$ref": "#/components/schemas/OpenAIResponseObject" + }, + "type": { + "type": "string", + "const": "response.completed", + "default": "response.completed" + } + }, + "additionalProperties": false, + "required": [ + "response", + "type" + ], + "title": "OpenAIResponseObjectStreamResponseCompleted" + }, + "OpenAIResponseObjectStreamResponseCreated": { + "type": "object", + "properties": { + "response": { + "$ref": "#/components/schemas/OpenAIResponseObject" + }, + "type": { + "type": "string", + "const": "response.created", + "default": "response.created" + } + }, + "additionalProperties": false, + "required": [ + "response", + "type" + ], + "title": "OpenAIResponseObjectStreamResponseCreated" + }, "CreateUploadSessionRequest": { "type": "object", "properties": { @@ -8891,8 +9415,7 @@ }, "additionalProperties": false, "required": [ - "role", - "content" + "role" ], "title": "OpenAIAssistantMessageParam", "description": "A message containing the model's (assistant) response in an OpenAI-compatible chat completion request." diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml index cf657bff9..cb73919d9 100644 --- a/docs/_static/llama-stack-spec.yaml +++ b/docs/_static/llama-stack-spec.yaml @@ -330,6 +330,39 @@ paths: schema: $ref: '#/components/schemas/CreateAgentTurnRequest' required: true + /v1/openai/v1/responses: + post: + responses: + '200': + description: >- + Runtime representation of an annotated type. + content: + application/json: + schema: + $ref: '#/components/schemas/OpenAIResponseObject' + text/event-stream: + schema: + $ref: '#/components/schemas/OpenAIResponseObjectStream' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Agents + description: Create a new OpenAI response. + parameters: [] + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/CreateOpenaiResponseRequest' + required: true /v1/files: get: responses: @@ -875,6 +908,36 @@ paths: required: true schema: type: string + /v1/openai/v1/responses/{id}: + get: + responses: + '200': + description: An OpenAIResponseObject. + content: + application/json: + schema: + $ref: '#/components/schemas/OpenAIResponseObject' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Agents + description: Retrieve an OpenAI response by its ID. + parameters: + - name: id + in: path + description: >- + The ID of the OpenAI response to retrieve. + required: true + schema: + type: string /v1/scoring-functions/{scoring_fn_id}: get: responses: @@ -3686,18 +3749,29 @@ components: default: 10 model: type: string + description: >- + The model identifier to use for the agent instructions: type: string + description: The system instructions for the agent + name: + type: string + description: >- + Optional name for the agent, used in telemetry and identification enable_session_persistence: type: boolean default: false + description: >- + Optional flag indicating whether session data has to be persisted response_format: $ref: '#/components/schemas/ResponseFormat' + description: Optional response format configuration additionalProperties: false required: - model - instructions title: AgentConfig + description: Configuration for an agent. AgentTool: oneOf: - type: string @@ -4318,6 +4392,295 @@ components: - event_type - turn_id title: AgentTurnResponseTurnStartPayload + OpenAIResponseInputMessage: + type: object + properties: + content: + oneOf: + - type: string + - type: array + items: + $ref: '#/components/schemas/OpenAIResponseInputMessageContent' + role: + oneOf: + - type: string + const: system + - type: string + const: developer + - type: string + const: user + - type: string + const: assistant + type: + type: string + const: message + default: message + additionalProperties: false + required: + - content + - role + title: OpenAIResponseInputMessage + OpenAIResponseInputMessageContent: + oneOf: + - $ref: '#/components/schemas/OpenAIResponseInputMessageContentText' + - $ref: '#/components/schemas/OpenAIResponseInputMessageContentImage' + discriminator: + propertyName: type + mapping: + input_text: '#/components/schemas/OpenAIResponseInputMessageContentText' + input_image: '#/components/schemas/OpenAIResponseInputMessageContentImage' + OpenAIResponseInputMessageContentImage: + type: object + properties: + detail: + oneOf: + - type: string + const: low + - type: string + const: high + - type: string + const: auto + default: auto + type: + type: string + const: input_image + default: input_image + image_url: + type: string + additionalProperties: false + required: + - detail + - type + title: OpenAIResponseInputMessageContentImage + OpenAIResponseInputMessageContentText: + type: object + properties: + text: + type: string + type: + type: string + const: input_text + default: input_text + additionalProperties: false + required: + - text + - type + title: OpenAIResponseInputMessageContentText + OpenAIResponseInputTool: + type: object + properties: + type: + oneOf: + - type: string + const: web_search + - type: string + const: web_search_preview_2025_03_11 + default: web_search + search_context_size: + type: string + default: medium + additionalProperties: false + required: + - type + title: OpenAIResponseInputToolWebSearch + CreateOpenaiResponseRequest: + type: object + properties: + input: + oneOf: + - type: string + - type: array + items: + $ref: '#/components/schemas/OpenAIResponseInputMessage' + description: Input message(s) to create the response. + model: + type: string + description: The underlying LLM used for completions. + previous_response_id: + type: string + description: >- + (Optional) if specified, the new response will be a continuation of the + previous response. This can be used to easily fork-off new responses from + existing responses. + store: + type: boolean + stream: + type: boolean + temperature: + type: number + tools: + type: array + items: + $ref: '#/components/schemas/OpenAIResponseInputTool' + additionalProperties: false + required: + - input + - model + title: CreateOpenaiResponseRequest + OpenAIResponseError: + type: object + properties: + code: + type: string + message: + type: string + additionalProperties: false + required: + - code + - message + title: OpenAIResponseError + OpenAIResponseObject: + type: object + properties: + created_at: + type: integer + error: + $ref: '#/components/schemas/OpenAIResponseError' + id: + type: string + model: + type: string + object: + type: string + const: response + default: response + output: + type: array + items: + $ref: '#/components/schemas/OpenAIResponseOutput' + parallel_tool_calls: + type: boolean + default: false + previous_response_id: + type: string + status: + type: string + temperature: + type: number + top_p: + type: number + truncation: + type: string + user: + type: string + additionalProperties: false + required: + - created_at + - id + - model + - object + - output + - parallel_tool_calls + - status + title: OpenAIResponseObject + OpenAIResponseOutput: + oneOf: + - $ref: '#/components/schemas/OpenAIResponseOutputMessage' + - $ref: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall' + discriminator: + propertyName: type + mapping: + message: '#/components/schemas/OpenAIResponseOutputMessage' + web_search_call: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall' + OpenAIResponseOutputMessage: + type: object + properties: + id: + type: string + content: + type: array + items: + $ref: '#/components/schemas/OpenAIResponseOutputMessageContent' + role: + type: string + const: assistant + default: assistant + status: + type: string + type: + type: string + const: message + default: message + additionalProperties: false + required: + - id + - content + - role + - status + - type + title: OpenAIResponseOutputMessage + OpenAIResponseOutputMessageContent: + type: object + properties: + text: + type: string + type: + type: string + const: output_text + default: output_text + additionalProperties: false + required: + - text + - type + title: >- + OpenAIResponseOutputMessageContentOutputText + "OpenAIResponseOutputMessageWebSearchToolCall": + type: object + properties: + id: + type: string + status: + type: string + type: + type: string + const: web_search_call + default: web_search_call + additionalProperties: false + required: + - id + - status + - type + title: >- + OpenAIResponseOutputMessageWebSearchToolCall + OpenAIResponseObjectStream: + oneOf: + - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated' + - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted' + discriminator: + propertyName: type + mapping: + response.created: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated' + response.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted' + "OpenAIResponseObjectStreamResponseCompleted": + type: object + properties: + response: + $ref: '#/components/schemas/OpenAIResponseObject' + type: + type: string + const: response.completed + default: response.completed + additionalProperties: false + required: + - response + - type + title: >- + OpenAIResponseObjectStreamResponseCompleted + "OpenAIResponseObjectStreamResponseCreated": + type: object + properties: + response: + $ref: '#/components/schemas/OpenAIResponseObject' + type: + type: string + const: response.created + default: response.created + additionalProperties: false + required: + - response + - type + title: >- + OpenAIResponseObjectStreamResponseCreated CreateUploadSessionRequest: type: object properties: @@ -6097,7 +6460,6 @@ components: additionalProperties: false required: - role - - content title: OpenAIAssistantMessageParam description: >- A message containing the model's (assistant) response in an OpenAI-compatible diff --git a/docs/getting_started_llama_api.ipynb b/docs/getting_started_llama_api.ipynb new file mode 100644 index 000000000..128e9114a --- /dev/null +++ b/docs/getting_started_llama_api.ipynb @@ -0,0 +1,907 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c1e7571c", + "metadata": { + "id": "c1e7571c" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb)\n", + "\n", + "# Getting Started with Llama 4 in Llama Stack\n", + "\n", + "\"drawing\"\n", + "\n", + "[Llama Stack](https://github.com/meta-llama/llama-stack) defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Service Providers providing their implementations.\n", + "\n", + "Read more about the project here: https://llama-stack.readthedocs.io/en/latest/index.html\n", + "\n", + "In this guide, we will showcase how you can get started with using Llama 4 in Llama Stack.\n" + ] + }, + { + "cell_type": "markdown", + "id": "4CV1Q19BDMVw", + "metadata": { + "id": "4CV1Q19BDMVw" + }, + "source": [ + "## 1. Getting started with Llama Stack" + ] + }, + { + "cell_type": "markdown", + "id": "K4AvfUAJZOeS", + "metadata": { + "id": "K4AvfUAJZOeS" + }, + "source": [ + "### 1.1. Create Llama API account\n", + "\n", + "In this showcase, we will use [Llama API](https://llama.developer.meta.com/) as the inference provider. So, you would first get an API key from Llama API if you don't have one already.\n", + "\n", + "\n", + "\n", + "> **Note:** Set the API Key in the Secrets of this notebook\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "oDUB7M_qe-Gs", + "metadata": { + "id": "oDUB7M_qe-Gs" + }, + "source": [ + "### 1.2. Setup and Running a Llama Stack server\n", + "\n", + "Llama Stack is architected as a collection of APIs that provide developers with the building blocks to build AI applications. \n", + "\n", + "Llama stack is typically available as a server with an endpoint that you can make calls to. Partners like Together and Fireworks offer their own Llama Stack compatible endpoints.\n", + "\n", + "In this showcase, we will start a Llama Stack server that is running locally.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "J2kGed0R5PSf", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "J2kGed0R5PSf", + "outputId": "2478ea60-8d35-48a1-b011-f233831740c5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: uv in /opt/homebrew/Caskroom/miniconda/base/envs/l4/lib/python3.10/site-packages (0.6.12)\n", + "\u001b[2mUsing Python 3.10.16 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/l4\u001b[0m\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 83ms\u001b[0m\u001b[0m\n", + "Environment '/Users/erichuang/projects/internal-llama-stack/.venv' already exists, re-using it.\n", + "Virtual environment 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packages...\u001b[0m (0/6)----\u001b[0m\u001b[0m 2.62 MiB/3.08 MiB \u001b[4A\n", + "\u001b[2mtyper \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 30.88 KiB/44.00 KiB\n", + "\u001b[2mtogether \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 32.00 KiB/85.81 KiB\n", + "\u001b[2K\u001b[3A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)----\u001b[0m\u001b[0m 2.62 MiB/3.08 MiB \u001b[3A\n", + "\u001b[2mtyper \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 44.00 KiB/44.00 KiB\n", + "\u001b[2mtogether \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 32.00 KiB/85.81 KiB\n", + "\u001b[2K\u001b[3A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)----\u001b[0m\u001b[0m 2.62 MiB/3.08 MiB \u001b[3A\n", + "\u001b[2mtogether \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 32.00 KiB/85.81 KiB\n", + "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)2m--\u001b[0m\u001b[0m 2.80 MiB/3.08 MiB \u001b[2A\n", + "\u001b[2mtogether \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 48.00 KiB/85.81 KiB\n", + "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)2m--\u001b[0m\u001b[0m 2.81 MiB/3.08 MiB \u001b[2A\n", + "\u001b[2K\u001b[1A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)----\u001b[0m\u001b[0m 48.00 KiB/85.81 KiB \u001b[1A\n", + "\u001b[2K\u001b[1A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)2m--\u001b[0m\u001b[0m 80.00 KiB/85.81 KiB \u001b[1A\n", + "\u001b[2K\u001b[2mPrepared \u001b[1m6 packages\u001b[0m \u001b[2min 365ms\u001b[0m\u001b[0m \u001b[1A\n", + "\u001b[2K\u001b[2mInstalled \u001b[1m6 packages\u001b[0m \u001b[2min 50ms\u001b[0m\u001b[0m \u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1meval-type-backport\u001b[0m\u001b[2m==0.2.2\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mfaiss-cpu\u001b[0m\u001b[2m==1.10.0\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mshellingham\u001b[0m\u001b[2m==1.5.4\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mtabulate\u001b[0m\u001b[2m==0.9.0\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mtogether\u001b[0m\u001b[2m==1.5.5\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mtyper\u001b[0m\u001b[2m==0.15.2\u001b[0m\n", + "torch torchvision --index-url https://download.pytorch.org/whl/cpu\n", + "\u001b[2mUsing Python 3.11.11 environment at: /Users/erichuang/projects/internal-llama-stack/.venv\u001b[0m\n", + "\u001b[2mAudited \u001b[1m2 packages\u001b[0m \u001b[2min 32ms\u001b[0m\u001b[0m\n", + "sentence-transformers --no-deps\n", + "\u001b[2mUsing Python 3.11.11 environment at: /Users/erichuang/projects/internal-llama-stack/.venv\u001b[0m\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 63ms\u001b[0m\u001b[0m\n", + "\u001b[32mBuild Successful!\u001b[0m\n" + ] + } + ], + "source": [ + "import os \n", + "import subprocess\n", + "import time\n", + "\n", + "!pip install uv \n", + "!uv pip install requests\n", + "\n", + "if \"UV_SYSTEM_PYTHON\" in os.environ:\n", + " del os.environ[\"UV_SYSTEM_PYTHON\"]\n", + "\n", + "# this command installs all the dependencies needed for the llama stack server \n", + "!uv run --with llama-stack llama stack build --template llama_api --image-type venv \n", + "\n", + "def run_llama_stack_server_background():\n", + " log_file = open(\"llama_stack_server.log\", \"w\")\n", + " process = subprocess.Popen(\n", + " \"uv run --with llama-stack llama stack run llama_api --image-type venv\",\n", + " shell=True,\n", + " stdout=log_file,\n", + " stderr=log_file,\n", + " text=True\n", + " )\n", + " \n", + " print(f\"Starting Llama Stack server with PID: {process.pid}\")\n", + " return process\n", + "\n", + "def wait_for_server_to_start():\n", + " import requests\n", + " from requests.exceptions import ConnectionError\n", + " import time\n", + " \n", + " url = \"http://0.0.0.0:8321/v1/health\"\n", + " max_retries = 30\n", + " retry_interval = 1\n", + " \n", + " print(\"Waiting for server to start\", end=\"\")\n", + " for _ in range(max_retries):\n", + " try:\n", + " response = requests.get(url)\n", + " if response.status_code == 200:\n", + " print(\"\\nServer is ready!\")\n", + " return True\n", + " except ConnectionError:\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(retry_interval)\n", + " \n", + " print(\"\\nServer failed to start after\", max_retries * retry_interval, \"seconds\")\n", + " return False\n", + "\n", + "\n", + "# use this helper if needed to kill the server \n", + "def kill_llama_stack_server():\n", + " # Kill any existing llama stack server processes\n", + " os.system(\"ps aux | grep -v grep | grep llama_stack.distribution.server.server | awk '{print $2}' | xargs kill -9\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c40e9efd", + "metadata": {}, + "source": [ + "### 1.3 Starting the Llama Stack Server" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f779283d", + "metadata": {}, + "outputs": [], + "source": [ + "server_process = run_llama_stack_server_background()\n", + "assert wait_for_server_to_start()" + ] + }, + { + "cell_type": "markdown", + "id": "90eb721b", + "metadata": {}, + "source": [ + "### 1.4 Install and Configure the Client\n", + "\n", + "Now that we have our Llama Stack server running locally, we need to install the client package to interact with it. The `llama-stack-client` provides a simple Python interface to access all the functionality of Llama Stack, including:\n", + "\n", + "- Chat Completions ( text and multimodal )\n", + "- Safety Shields \n", + "- Agent capabilities with tools like web search, RAG with Telemetry\n", + "- Evaluation and scoring frameworks\n", + "\n", + "The client handles all the API communication with our local server, making it easy to integrate Llama Stack's capabilities into your applications.\n", + "\n", + "In the next cells, we'll:\n", + "\n", + "1. Install the client package\n", + "2. Set up API keys for external services (Together AI and Tavily Search)\n", + "3. Initialize the client to connect to our local server\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2e68e32a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2mUsing Python 3.10.16 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/stack\u001b[0m\n", + "\u001b[2K\u001b[2mResolved \u001b[1m31 packages\u001b[0m \u001b[2min 284ms\u001b[0m\u001b[0m \u001b[0m\n", + "\u001b[2mAudited \u001b[1m31 packages\u001b[0m \u001b[2min 0.04ms\u001b[0m\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install -U llama-stack-client" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "E1UFuJC570Tk", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "75307e3dee604d30aa44713e6e293e64", + "5ce87402a79342af995df41ac3940d55", + "fbbcc19886cc43b38424fbb184162c61", + "29212208db6b432eb4f708cd64258954", + "50dd8994a4cf486ebbec5ffd4322992a", + 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"774b513d64524ac7823a2cf13efa8d41", + "1e56da93bcf64ff490416d2b66cd3dc0", + "b7e35038ce344110b785753b655130f5", + "5472af91737446f4a4a2d92a3f684a45", + "9fb4368802da4a5a8101ba200d98403a", + "2e713bcc372e48b2a006558db4d1df68", + "1a277abd5ea44253bc6894bef258b52b", + "b3eedd82e7da4ce8b3ded70e49a2afd0", + "6f5c18cb8002471f8b3764effee37324", + "3bebac362b344e8d9103c5011613f1ea", + "670905a55b19458da69f83c8bcd511d1", + "ff54451a48394faaaa9d8cdb690d0718", + "36b5bc19b2d0407f8ab28ff0da2ce12d", + "879e48d9a9e04183903d94ffe98313d2", + "abce503d70594c2ca9afdc47847c125b", + "028e291ee53947bbbbc4bfb68c695f5f", + "a530662719374c95a9bef12e59e28c85", + "bffc0f4b12f141398535990709fd4f2c", + "04804c74e1dd43449d5f758cf5d0ba5e", + "95a506c3007c4525b01ee4e1600d671b", + "a0d6b0caeb2340fe96c8f5569e3d3ae4", + "30798f87a8b848d783fdacd71af5dc04", + "07ce54c75e76488ba4019a20b3707061", + "f023175de68445f98a6b01bb40ccdc6d", + "7389b79a0ff44cd68c7866995d728023", + "8e2b70ffe4eb4974bd6393fcc1292267", + "13eee164dc534424acb9dc9ee37a9465", + "722a7fe16af3422585a20c651345cfa4", + "f5596c1c9c4d42f3bc171961f9582eff", + "85d66e615b5742e78657b1e60c75fc72", + "731c02dc5dd446c3b22765575148e256", + "254ce460ce244c99a5afe39d5d51f6b7", + "4cf1dc345ace4da59f978f661487f975", + "8f30fca71bf24e5ca26e17c2321f893c", + "dd85d37dd1d14c7ea4592f8e11b2d2c8", + "3cb06377e4454f009d6b2aa7aa6ff0a9", + "4502477db4d948e693012364c2dcb370", + "52fe404ec9c14db2a7279b4c154eef3d" + ] + }, + "collapsed": true, + "id": "E1UFuJC570Tk", + "outputId": "aebb69d4-c167-4de5-eb8a-dd19dd538f63" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not in Google Colab environment\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "try:\n", + " from google.colab import userdata\n", + " os.environ['LLAMA_API_KEY'] = userdata.get('LLAMA_API_KEY')\n", + "except ImportError:\n", + " print(\"Not in Google Colab environment\")\n", + "\n", + "for key in ['LLAMA_API_KEY']:\n", + " try:\n", + " api_key = os.environ[key]\n", + " if not api_key:\n", + " raise ValueError(f\"{key} environment variable is empty\")\n", + " except KeyError:\n", + " api_key = input(f\"{key} environment variable is not set. Please enter your API key: \")\n", + " os.environ[key] = api_key\n", + "\n", + "from llama_stack_client import LlamaStackClient\n", + "\n", + "client = LlamaStackClient(\n", + " base_url=\"http://0.0.0.0:8321\", \n", + " provider_data = {\n", + " \"llama_api_key\": os.environ['LLAMA_API_KEY']\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "635a7a6f", + "metadata": {}, + "source": [ + "Now that we have completed the setup and configuration, let's start exploring the capabilities of Llama 4!\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "0fc75d73", + "metadata": {}, + "source": [ + "## 2. Running Llama 4" + ] + }, + { + "cell_type": "markdown", + "id": "7dacaa2d-94e9-42e9-82a0-73522dfc7010", + "metadata": { + "id": "7dacaa2d-94e9-42e9-82a0-73522dfc7010" + }, + "source": [ + "### 2.1 Check available models\n", + "\n", + "All the models available are programmatically accessible via the client." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ruO9jQna_t_S", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "ruO9jQna_t_S", + "outputId": "ab1722a7-62ab-43bb-9cab-4e45bf62068a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available models:\n", + "- Llama-3.1-8B-Instruct\n", + "- meta-llama/Llama-3.1-8B-Instruct\n", + "- Llama-3.2-11B-Vision-Instruct\n", + "- meta-llama/Llama-3.2-11B-Vision-Instruct\n", + "- Llama-3.3-70B-Instruct\n", + "- meta-llama/Llama-3.3-70B-Instruct\n", + "- Llama-4-Maverick-17B-128E-Instruct-FP8\n", + "- meta-llama/Llama-4-Maverick-17B-128E-Instruct\n", + "- all-MiniLM-L6-v2\n" + ] + } + ], + "source": [ + "from rich.pretty import pprint\n", + "\n", + "print(\"Available models:\")\n", + "for m in client.models.list():\n", + " print(f\"- {m.identifier}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "86366383", + "metadata": { + "id": "86366383" + }, + "source": [ + "### 2.2 Run a simple chat completion with one of the models\n", + "\n", + "We will test the client by doing a simple chat completion." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "77c29dba", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "77c29dba", + "outputId": "4857974f-4c70-4bc4-f90a-6ae49dc9c41e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here is a two-sentence poem about a llama:\n", + "\n", + "With soft fur and gentle eyes, the llama roams with gentle surprise, a peaceful presence in the Andean skies. Its calm demeanor and soft humming song bring serenity to all who belong.\n" + ] + } + ], + "source": [ + "# TODO: update this with a vision model\n", + "model_id = \"meta-llama/Llama-4-Maverick-17B-128E-Instruct\"\n", + "\n", + "response = client.inference.chat_completion(\n", + " model_id=model_id,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\n", + " {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"},\n", + " ],\n", + ")\n", + "\n", + "print(response.completion_message.content)\n" + ] + }, + { + "cell_type": "markdown", + "id": "7737cd41", + "metadata": {}, + "source": [ + "### 2.3 Running multimodal inference" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e7b1baa7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 275k 100 275k 0 0 847k 0 --:--:-- --:--:-- --:--:-- 845k--:--:-- --:--:-- 0\n" + ] + }, + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": { + "image/jpeg": { + "height": 256, + "width": 256 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "!curl -O https://raw.githubusercontent.com/meta-llama/llama-models/refs/heads/main/Llama_Repo.jpeg\n", + "\n", + "from IPython.display import Image\n", + "Image(\"Llama_Repo.jpeg\", width=256, height=256)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e1450ecc", + "metadata": {}, + "outputs": [], + "source": [ + "import base64\n", + "def encode_image(image_path):\n", + " with open(image_path, \"rb\") as image_file:\n", + " base64_string = base64.b64encode(image_file.read()).decode(\"utf-8\")\n", + " base64_url = f\"data:image/png;base64,{base64_string}\"\n", + " return base64_url" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d7914894", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The image features three llamas, each with a distinct color. The llama on the left is white, the middle one is purple, and the one on the right is also white but wears a blue party hat.\n", + "\n", + "To determine the number of different colors present, we can count the unique hues:\n", + "\n", + "1. White (two llamas)\n", + "2. Purple (one llama)\n", + "3. Blue (party hat)\n", + "\n", + "Therefore, there are 3 different colors visible in the image: white, purple, and blue.\n" + ] + } + ], + "source": [ + "response = client.inference.chat_completion(\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"image\",\n", + " \"image\": {\n", + " \"url\": {\n", + " \"uri\": encode_image(\"Llama_Repo.jpeg\")\n", + " }\n", + " }\n", + " },\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": \"How many different colors are those llamas? What are those colors?\",\n", + " }\n", + " ]\n", + " }\n", + " ],\n", + " model_id=model_id,\n", + " stream=False,\n", + ")\n", + "\n", + "print(response.completion_message.content)" + ] + }, + { + "cell_type": "markdown", + "id": "8cf0d555", + "metadata": { + "id": "8cf0d555" + }, + "source": [ + "### 2.4 Have a conversation\n", + "\n", + "Maintaining a conversation history allows the model to retain context from previous interactions. Use a list to accumulate messages, enabling continuity throughout the chat session." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3fdf9df6", + "metadata": { + "id": "3fdf9df6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m> Response: The most famous Prime Minister of England during World War 2 was Winston Churchill. He served as the Prime Minister of the United Kingdom from 1940 to 1945, and again from 1951 to 1955. Churchill is widely regarded as one of the greatest wartime leaders in history, known for his leadership, oratory skills, and unwavering resolve during the war.\n", + "\n", + "Churchill played a crucial role in rallying the British people during the war, and his speeches, such as the \"We shall fight on the beaches\" and \"Their finest hour\" speeches, are still remembered and celebrated today. He worked closely with other Allied leaders, including US President Franklin D. Roosevelt and Soviet leader Joseph Stalin, to coordinate the war effort and ultimately secure the defeat of Nazi Germany.\n", + "\n", + "Churchill's leadership and legacy have endured long after the war, and he remains one of the most iconic and influential figures in British history.\u001b[0m\n", + "\u001b[36m> Response: Winston Churchill was known for his many memorable quotes, but one of his most famous is:\n", + "\n", + "**\"We shall fight on the beaches, we shall fight on the landing grounds, we shall fight in the fields and in the streets, we shall fight in the hills; we shall never surrender.\"**\n", + "\n", + "This quote is from his speech to the House of Commons on June 4, 1940, during the early stages of World War II, when Nazi Germany was threatening to invade Britain. The speech is known as the \"We Shall Fight on the Beaches\" speech, and it's considered one of the greatest speeches of the 20th century.\n", + "\n", + "However, if I had to pick a single, even more concise quote, it would be:\n", + "\n", + "**\"Blood, toil, tears, and sweat.\"**\n", + "\n", + "This was the opening phrase of his first speech as Prime Minister to the House of Commons on May 13, 1940, in which he said:\n", + "\n", + "\"I say to the House as I said to those who have joined this Government, I have nothing to offer but blood, toil, tears, and sweat. We have before us an ordeal of the most grievous kind.\"\n", + "\n", + "This quote has become synonymous with Churchill's leadership and resolve during the war.\u001b[0m\n" + ] + } + ], + "source": [ + "from termcolor import cprint\n", + "\n", + "questions = [\n", + " \"Who was the most famous PM of England during world war 2 ?\",\n", + " \"What was his most famous quote ?\"\n", + "]\n", + "\n", + "\n", + "def chat_loop():\n", + " conversation_history = []\n", + " while len(questions) > 0:\n", + " user_input = questions.pop(0)\n", + " if user_input.lower() in [\"exit\", \"quit\", \"bye\"]:\n", + " cprint(\"Ending conversation. Goodbye!\", \"yellow\")\n", + " break\n", + "\n", + " user_message = {\"role\": \"user\", \"content\": user_input}\n", + " conversation_history.append(user_message)\n", + "\n", + " response = client.inference.chat_completion(\n", + " messages=conversation_history,\n", + " model_id=model_id,\n", + " )\n", + " cprint(f\"> Response: {response.completion_message.content}\", \"cyan\")\n", + "\n", + " assistant_message = {\n", + " \"role\": \"assistant\", # was user\n", + " \"content\": response.completion_message.content,\n", + " \"stop_reason\": response.completion_message.stop_reason,\n", + " }\n", + " conversation_history.append(assistant_message)\n", + "\n", + "\n", + "chat_loop()\n" + ] + }, + { + "cell_type": "markdown", + "id": "72e5111e", + "metadata": { + "id": "72e5111e" + }, + "source": [ + "Here is an example for you to try a conversation yourself.\n", + "Remember to type `quit` or `exit` after you are done chatting." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "9496f75c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9496f75c", + "outputId": "7d93a4cf-a5d4-4741-b6eb-6bce3a27ff66" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m> Response: Hello! How are you today? Is there something I can help you with or would you like to chat?\u001b[0m\n", + "\u001b[33mEnding conversation. Goodbye!\u001b[0m\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "from termcolor import cprint\n", + "\n", + "def chat_loop():\n", + " conversation_history = []\n", + " while True:\n", + " user_input = input(\"User> \")\n", + " if user_input.lower() in [\"exit\", \"quit\", \"bye\"]:\n", + " cprint(\"Ending conversation. Goodbye!\", \"yellow\")\n", + " break\n", + "\n", + " user_message = {\"role\": \"user\", \"content\": user_input}\n", + " conversation_history.append(user_message)\n", + "\n", + " response = client.inference.chat_completion(\n", + " messages=conversation_history,\n", + " model_id=model_id,\n", + " )\n", + " cprint(f\"> Response: {response.completion_message.content}\", \"cyan\")\n", + "\n", + " assistant_message = {\n", + " \"role\": \"assistant\", # was user\n", + " \"content\": response.completion_message.content,\n", + " \"stop_reason\": response.completion_message.stop_reason,\n", + " }\n", + " conversation_history.append(assistant_message)\n", + "\n", + "\n", + "chat_loop()\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "l4", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 + } diff --git a/docs/make.bat b/docs/make.bat index 32bb24529..954237b9b 100644 --- a/docs/make.bat +++ b/docs/make.bat @@ -1,35 +1,35 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/openapi_generator/pyopenapi/generator.py b/docs/openapi_generator/pyopenapi/generator.py index 3936bb3c4..cc594d8d7 100644 --- a/docs/openapi_generator/pyopenapi/generator.py +++ b/docs/openapi_generator/pyopenapi/generator.py @@ -6,6 +6,7 @@ import hashlib import ipaddress +import types import typing from dataclasses import make_dataclass from typing import Any, Dict, Set, Union @@ -179,7 +180,7 @@ class ContentBuilder: "Creates the content subtree for a request or response." def is_iterator_type(t): - return "StreamChunk" in str(t) + return "StreamChunk" in str(t) or "OpenAIResponseObjectStream" in str(t) def get_media_type(t): if is_generic_list(t): @@ -189,7 +190,7 @@ class ContentBuilder: else: return "application/json" - if typing.get_origin(payload_type) is typing.Union: + if typing.get_origin(payload_type) in (typing.Union, types.UnionType): media_types = [] item_types = [] for x in typing.get_args(payload_type): diff --git a/docs/requirements.txt b/docs/requirements.txt index e31d08ff1..6cd45c33b 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,16 +1,16 @@ -sphinx==8.1.3 -myst-parser linkify +myst-parser -e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme -sphinx-rtd-theme>=1.0.0 -sphinx_autobuild +sphinx==8.1.3 sphinx-copybutton sphinx-design sphinx-pdj-theme -sphinx_rtd_dark_mode +sphinx-rtd-theme>=1.0.0 sphinx-tabs +sphinx_autobuild +sphinx_rtd_dark_mode +sphinxcontrib-mermaid sphinxcontrib-openapi sphinxcontrib-redoc -sphinxcontrib-mermaid sphinxcontrib-video tomli diff --git a/docs/source/building_applications/rag.md b/docs/source/building_applications/rag.md index 39d1ba333..db6303209 100644 --- a/docs/source/building_applications/rag.md +++ b/docs/source/building_applications/rag.md @@ -68,7 +68,8 @@ chunks_response = client.vector_io.query( ### Using the RAG Tool A better way to ingest documents is to use the RAG Tool. This tool allows you to ingest documents from URLs, files, etc. -and automatically chunks them into smaller pieces. +and automatically chunks them into smaller pieces. More examples for how to format a RAGDocument can be found in the +[appendix](#more-ragdocument-examples). ```python from llama_stack_client import RAGDocument @@ -178,3 +179,38 @@ for vector_db_id in client.vector_dbs.list(): print(f"Unregistering vector database: {vector_db_id.identifier}") client.vector_dbs.unregister(vector_db_id=vector_db_id.identifier) ``` + +### Appendix + +#### More RAGDocument Examples +```python +from llama_stack_client import RAGDocument +import base64 + +RAGDocument(document_id="num-0", content={"uri": "file://path/to/file"}) +RAGDocument(document_id="num-1", content="plain text") +RAGDocument( + document_id="num-2", + content={ + "type": "text", + "text": "plain text input", + }, # for inputs that should be treated as text explicitly +) +RAGDocument( + document_id="num-3", + content={ + "type": "image", + "image": {"url": {"uri": "https://mywebsite.com/image.jpg"}}, + }, +) +B64_ENCODED_IMAGE = base64.b64encode( + requests.get( + "https://raw.githubusercontent.com/meta-llama/llama-stack/refs/heads/main/docs/_static/llama-stack.png" + ).content +) +RAGDocuemnt( + document_id="num-4", + content={"type": "image", "image": {"data": B64_ENCODED_IMAGE}}, +) +``` +for more strongly typed interaction use the typed dicts found [here](https://github.com/meta-llama/llama-stack-client-python/blob/38cd91c9e396f2be0bec1ee96a19771582ba6f17/src/llama_stack_client/types/shared_params/document.py). diff --git a/docs/source/building_applications/tools.md b/docs/source/building_applications/tools.md index 94841a773..95c69ffa3 100644 --- a/docs/source/building_applications/tools.md +++ b/docs/source/building_applications/tools.md @@ -41,30 +41,9 @@ client.toolgroups.register( The tool requires an API key which can be provided either in the configuration or through the request header `X-LlamaStack-Provider-Data`. The format of the header is `{"_api_key": }`. +> **NOTE:** When using Tavily Search and Bing Search, the inference output will still display "Brave Search." This is because Llama models have been trained with Brave Search as a built-in tool. Tavily and bing is just being used in lieu of Brave search. -#### Code Interpreter - -The Code Interpreter allows execution of Python code within a controlled environment. - -```python -# Register Code Interpreter tool group -client.toolgroups.register( - toolgroup_id="builtin::code_interpreter", provider_id="code_interpreter" -) -``` - -Features: -- Secure execution environment using `bwrap` sandboxing -- Matplotlib support for generating plots -- Disabled dangerous system operations -- Configurable execution timeouts - -> ⚠️ Important: The code interpreter tool can operate in a controlled environment locally or on Podman containers. To ensure proper functionality in containerized environments: -> - The container requires privileged access (e.g., --privileged). -> - Users without sufficient permissions may encounter permission errors. (`bwrap: Can't mount devpts on /newroot/dev/pts: Permission denied`) -> - 🔒 Security Warning: Privileged mode grants elevated access and bypasses security restrictions. Use only in local, isolated, or controlled environments. - #### WolframAlpha The WolframAlpha tool provides access to computational knowledge through the WolframAlpha API. @@ -102,7 +81,7 @@ Features: - Context retrieval with token limits -> **Note:** By default, llama stack run.yaml defines toolgroups for web search, code interpreter and rag, that are provided by tavily-search, code-interpreter and rag providers. +> **Note:** By default, llama stack run.yaml defines toolgroups for web search, wolfram alpha and rag, that are provided by tavily-search, wolfram-alpha and rag providers. ## Model Context Protocol (MCP) Tools @@ -214,3 +193,69 @@ response = agent.create_turn( session_id=session_id, ) ``` +## Simple Example 2: Using an Agent with the Web Search Tool +1. Start by registering a Tavily API key at [Tavily](https://tavily.com/). +2. [Optional] Provide the API key directly to the Llama Stack server +```bash +export TAVILY_SEARCH_API_KEY="your key" +``` +```bash +--env TAVILY_SEARCH_API_KEY=${TAVILY_SEARCH_API_KEY} +``` +3. Run the following script. +```python +from llama_stack_client.lib.agents.agent import Agent +from llama_stack_client.types.agent_create_params import AgentConfig +from llama_stack_client.lib.agents.event_logger import EventLogger +from llama_stack_client import LlamaStackClient + +client = LlamaStackClient( + base_url=f"http://localhost:8321", + provider_data={ + "tavily_search_api_key": "your_TAVILY_SEARCH_API_KEY" + }, # Set this from the client side. No need to provide it if it has already been configured on the Llama Stack server. +) + +agent = Agent( + client, + model="meta-llama/Llama-3.2-3B-Instruct", + instructions=( + "You are a web search assistant, must use websearch tool to look up the most current and precise information available. " + ), + tools=["builtin::websearch"], +) + +session_id = agent.create_session("websearch-session") + +response = agent.create_turn( + messages=[ + {"role": "user", "content": "How did the USA perform in the last Olympics?"} + ], + session_id=session_id, +) +for log in EventLogger().log(response): + log.print() +``` + +## Simple Example3: Using an Agent with the WolframAlpha Tool +1. Start by registering for a WolframAlpha API key at [WolframAlpha Developer Portal](https://developer.wolframalpha.com/access). +2. Provide the API key either when starting the Llama Stack server: + ```bash + --env WOLFRAM_ALPHA_API_KEY=${WOLFRAM_ALPHA_API_KEY} + ``` + or from the client side: + ```python + client = LlamaStackClient( + base_url="http://localhost:8321", + provider_data={"wolfram_alpha_api_key": wolfram_api_key}, + ) + ``` +3. Configure the tools in the Agent by setting `tools=["builtin::wolfram_alpha"]`. +4. Example user query: + ```python + response = agent.create_turn( + messages=[{"role": "user", "content": "Solve x^2 + 2x + 1 = 0 using WolframAlpha"}], + session_id=session_id, + ) + ``` +``` diff --git a/docs/source/distributions/building_distro.md b/docs/source/distributions/building_distro.md index ad5d3bff4..56b8d30a8 100644 --- a/docs/source/distributions/building_distro.md +++ b/docs/source/distributions/building_distro.md @@ -109,8 +109,6 @@ llama stack build --list-templates +------------------------------+-----------------------------------------------------------------------------+ | nvidia | Use NVIDIA NIM for running LLM inference | +------------------------------+-----------------------------------------------------------------------------+ -| meta-reference-quantized-gpu | Use Meta Reference with fp8, int4 quantization for running LLM inference | -+------------------------------+-----------------------------------------------------------------------------+ | cerebras | Use Cerebras for running LLM inference | +------------------------------+-----------------------------------------------------------------------------+ | ollama | Use (an external) Ollama server for running LLM inference | @@ -176,7 +174,11 @@ distribution_spec: safety: inline::llama-guard agents: inline::meta-reference telemetry: inline::meta-reference +image_name: ollama image_type: conda + +# If some providers are external, you can specify the path to the implementation +external_providers_dir: /etc/llama-stack/providers.d ``` ``` @@ -184,6 +186,57 @@ llama stack build --config llama_stack/templates/ollama/build.yaml ``` ::: +:::{tab-item} Building with External Providers + +Llama Stack supports external providers that live outside of the main codebase. This allows you to create and maintain your own providers independently or use community-provided providers. + +To build a distribution with external providers, you need to: + +1. Configure the `external_providers_dir` in your build configuration file: + +```yaml +# Example my-external-stack.yaml with external providers +version: '2' +distribution_spec: + description: Custom distro for CI tests + providers: + inference: + - remote::custom_ollama +# Add more providers as needed +image_type: container +image_name: ci-test +# Path to external provider implementations +external_providers_dir: /etc/llama-stack/providers.d +``` + +Here's an example for a custom Ollama provider: + +```yaml +adapter: + adapter_type: custom_ollama + pip_packages: + - ollama + - aiohttp + - llama-stack-provider-ollama # This is the provider package + config_class: llama_stack_ollama_provider.config.OllamaImplConfig + module: llama_stack_ollama_provider +api_dependencies: [] +optional_api_dependencies: [] +``` + +The `pip_packages` section lists the Python packages required by the provider, as well as the +provider package itself. The package must be available on PyPI or can be provided from a local +directory or a git repository (git must be installed on the build environment). + +2. Build your distribution using the config file: + +``` +llama stack build --config my-external-stack.yaml +``` + +For more information on external providers, including directory structure, provider types, and implementation requirements, see the [External Providers documentation](../providers/external.md). +::: + :::{tab-item} Building Container ```{admonition} Podman Alternative diff --git a/docs/source/distributions/configuration.md b/docs/source/distributions/configuration.md index c06632991..b62227a84 100644 --- a/docs/source/distributions/configuration.md +++ b/docs/source/distributions/configuration.md @@ -53,6 +53,13 @@ models: provider_id: ollama provider_model_id: null shields: [] +server: + port: 8321 + auth: + provider_type: "kubernetes" + config: + api_server_url: "https://kubernetes.default.svc" + ca_cert_path: "/path/to/ca.crt" ``` Let's break this down into the different sections. The first section specifies the set of APIs that the stack server will serve: @@ -102,6 +109,105 @@ A Model is an instance of a "Resource" (see [Concepts](../concepts/index)) and i What's with the `provider_model_id` field? This is an identifier for the model inside the provider's model catalog. Contrast it with `model_id` which is the identifier for the same model for Llama Stack's purposes. For example, you may want to name "llama3.2:vision-11b" as "image_captioning_model" when you use it in your Stack interactions. When omitted, the server will set `provider_model_id` to be the same as `model_id`. +## Server Configuration + +The `server` section configures the HTTP server that serves the Llama Stack APIs: + +```yaml +server: + port: 8321 # Port to listen on (default: 8321) + tls_certfile: "/path/to/cert.pem" # Optional: Path to TLS certificate for HTTPS + tls_keyfile: "/path/to/key.pem" # Optional: Path to TLS key for HTTPS + auth: # Optional: Authentication configuration + provider_type: "kubernetes" # Type of auth provider + config: # Provider-specific configuration + api_server_url: "https://kubernetes.default.svc" + ca_cert_path: "/path/to/ca.crt" # Optional: Path to CA certificate +``` + +### Authentication Configuration + +The `auth` section configures authentication for the server. When configured, all API requests must include a valid Bearer token in the Authorization header: + +``` +Authorization: Bearer +``` + +The server supports multiple authentication providers: + +#### Kubernetes Provider + +The Kubernetes cluster must be configured to use a service account for authentication. + +```bash +kubectl create namespace llama-stack +kubectl create serviceaccount llama-stack-auth -n llama-stack +kubectl create rolebinding llama-stack-auth-rolebinding --clusterrole=admin --serviceaccount=llama-stack:llama-stack-auth -n llama-stack +kubectl create token llama-stack-auth -n llama-stack > llama-stack-auth-token +``` + +Validates tokens against the Kubernetes API server: +```yaml +server: + auth: + provider_type: "kubernetes" + config: + api_server_url: "https://kubernetes.default.svc" # URL of the Kubernetes API server + ca_cert_path: "/path/to/ca.crt" # Optional: Path to CA certificate +``` + +The provider extracts user information from the JWT token: +- Username from the `sub` claim becomes a role +- Kubernetes groups become teams + +You can easily validate a request by running: + +```bash +curl -s -L -H "Authorization: Bearer $(cat llama-stack-auth-token)" http://127.0.0.1:8321/v1/providers +``` + +#### Custom Provider +Validates tokens against a custom authentication endpoint: +```yaml +server: + auth: + provider_type: "custom" + config: + endpoint: "https://auth.example.com/validate" # URL of the auth endpoint +``` + +The custom endpoint receives a POST request with: +```json +{ + "api_key": "", + "request": { + "path": "/api/v1/endpoint", + "headers": { + "content-type": "application/json", + "user-agent": "curl/7.64.1" + }, + "params": { + "key": ["value"] + } + } +} +``` + +And must respond with: +```json +{ + "access_attributes": { + "roles": ["admin", "user"], + "teams": ["ml-team", "nlp-team"], + "projects": ["llama-3", "project-x"], + "namespaces": ["research"] + }, + "message": "Authentication successful" +} +``` + +If no access attributes are returned, the token is used as a namespace. + ## Extending to handle Safety Configuring Safety can be a little involved so it is instructive to go through an example. diff --git a/docs/source/distributions/ondevice_distro/android_sdk.md b/docs/source/distributions/ondevice_distro/android_sdk.md index 4fa6eaf70..a097a2adf 100644 --- a/docs/source/distributions/ondevice_distro/android_sdk.md +++ b/docs/source/distributions/ondevice_distro/android_sdk.md @@ -24,7 +24,7 @@ The key files in the app are `ExampleLlamaStackLocalInference.kt`, `ExampleLlama Add the following dependency in your `build.gradle.kts` file: ``` dependencies { - implementation("com.llama.llamastack:llama-stack-client-kotlin:0.1.4.2") + implementation("com.llama.llamastack:llama-stack-client-kotlin:0.2.2") } ``` This will download jar files in your gradle cache in a directory like `~/.gradle/caches/modules-2/files-2.1/com.llama.llamastack/` @@ -37,11 +37,7 @@ For local inferencing, it is required to include the ExecuTorch library into you Include the ExecuTorch library by: 1. Download the `download-prebuilt-et-lib.sh` script file from the [llama-stack-client-kotlin-client-local](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release/llama-stack-client-kotlin-client-local/download-prebuilt-et-lib.sh) directory to your local machine. -2. Move the script to the top level of your Android app where the app directory resides: -

- -

- +2. Move the script to the top level of your Android app where the `app` directory resides. 3. Run `sh download-prebuilt-et-lib.sh` to create an `app/libs` directory and download the `executorch.aar` in that path. This generates an ExecuTorch library for the XNNPACK delegate. 4. Add the `executorch.aar` dependency in your `build.gradle.kts` file: ``` @@ -52,6 +48,8 @@ dependencies { } ``` +See other dependencies for the local RAG in Android app [README](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release/examples/android_app#quick-start). + ## Llama Stack APIs in Your Android App Breaking down the demo app, this section will show the core pieces that are used to initialize and run inference with Llama Stack using the Kotlin library. @@ -60,7 +58,7 @@ Start a Llama Stack server on localhost. Here is an example of how you can do th ``` conda create -n stack-fireworks python=3.10 conda activate stack-fireworks -pip install --no-cache llama-stack==0.1.4 +pip install --no-cache llama-stack==0.2.2 llama stack build --template fireworks --image-type conda export FIREWORKS_API_KEY= llama stack run fireworks --port 5050 diff --git a/docs/source/distributions/remote_hosted_distro/nvidia.md b/docs/source/distributions/remote_hosted_distro/nvidia.md deleted file mode 100644 index 58731392d..000000000 --- a/docs/source/distributions/remote_hosted_distro/nvidia.md +++ /dev/null @@ -1,88 +0,0 @@ - -# NVIDIA Distribution - -The `llamastack/distribution-nvidia` distribution consists of the following provider configurations. - -| API | Provider(s) | -|-----|-------------| -| agents | `inline::meta-reference` | -| datasetio | `inline::localfs` | -| eval | `inline::meta-reference` | -| inference | `remote::nvidia` | -| post_training | `remote::nvidia` | -| safety | `remote::nvidia` | -| scoring | `inline::basic` | -| telemetry | `inline::meta-reference` | -| tool_runtime | `inline::rag-runtime` | -| vector_io | `inline::faiss` | - - -### Environment Variables - -The following environment variables can be configured: - -- `NVIDIA_API_KEY`: NVIDIA API Key (default: ``) -- `NVIDIA_USER_ID`: NVIDIA User ID (default: `llama-stack-user`) -- `NVIDIA_DATASET_NAMESPACE`: NVIDIA Dataset Namespace (default: `default`) -- `NVIDIA_ACCESS_POLICIES`: NVIDIA Access Policies (default: `{}`) -- `NVIDIA_PROJECT_ID`: NVIDIA Project ID (default: `test-project`) -- `NVIDIA_CUSTOMIZER_URL`: NVIDIA Customizer URL (default: `https://customizer.api.nvidia.com`) -- `NVIDIA_OUTPUT_MODEL_DIR`: NVIDIA Output Model Directory (default: `test-example-model@v1`) -- `GUARDRAILS_SERVICE_URL`: URL for the NeMo Guardrails Service (default: `http://0.0.0.0:7331`) -- `INFERENCE_MODEL`: Inference model (default: `Llama3.1-8B-Instruct`) -- `SAFETY_MODEL`: Name of the model to use for safety (default: `meta/llama-3.1-8b-instruct`) - -### Models - -The following models are available by default: - -- `meta/llama3-8b-instruct (aliases: meta-llama/Llama-3-8B-Instruct)` -- `meta/llama3-70b-instruct (aliases: meta-llama/Llama-3-70B-Instruct)` -- `meta/llama-3.1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)` -- `meta/llama-3.1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)` -- `meta/llama-3.1-405b-instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)` -- `meta/llama-3.2-1b-instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)` -- `meta/llama-3.2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)` -- `meta/llama-3.2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)` -- `meta/llama-3.2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)` -- `nvidia/llama-3.2-nv-embedqa-1b-v2 ` -- `nvidia/nv-embedqa-e5-v5 ` -- `nvidia/nv-embedqa-mistral-7b-v2 ` -- `snowflake/arctic-embed-l ` - - -### Prerequisite: API Keys - -Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). - - -## Running Llama Stack with NVIDIA - -You can do this via Conda (build code) or Docker which has a pre-built image. - -### Via Docker - -This method allows you to get started quickly without having to build the distribution code. - -```bash -LLAMA_STACK_PORT=8321 -docker run \ - -it \ - --pull always \ - -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ - -v ./run.yaml:/root/my-run.yaml \ - llamastack/distribution-nvidia \ - --yaml-config /root/my-run.yaml \ - --port $LLAMA_STACK_PORT \ - --env NVIDIA_API_KEY=$NVIDIA_API_KEY -``` - -### Via Conda - -```bash -llama stack build --template nvidia --image-type conda -llama stack run ./run.yaml \ - --port 8321 \ - --env NVIDIA_API_KEY=$NVIDIA_API_KEY - --env INFERENCE_MODEL=$INFERENCE_MODEL -``` diff --git a/docs/source/distributions/remote_hosted_distro/watsonx.md b/docs/source/distributions/remote_hosted_distro/watsonx.md new file mode 100644 index 000000000..b7c89e9b0 --- /dev/null +++ b/docs/source/distributions/remote_hosted_distro/watsonx.md @@ -0,0 +1,88 @@ +--- +orphan: true +--- + +# watsonx Distribution + +```{toctree} +:maxdepth: 2 +:hidden: + +self +``` + +The `llamastack/distribution-watsonx` distribution consists of the following provider configurations. + +| API | Provider(s) | +|-----|-------------| +| agents | `inline::meta-reference` | +| datasetio | `remote::huggingface`, `inline::localfs` | +| eval | `inline::meta-reference` | +| inference | `remote::watsonx` | +| safety | `inline::llama-guard` | +| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | +| telemetry | `inline::meta-reference` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` | +| vector_io | `inline::faiss` | + + + +### Environment Variables + +The following environment variables can be configured: + +- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`) +- `WATSONX_API_KEY`: watsonx API Key (default: ``) +- `WATSONX_PROJECT_ID`: watsonx Project ID (default: ``) + +### Models + +The following models are available by default: + +- `meta-llama/llama-3-3-70b-instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)` +- `meta-llama/llama-2-13b-chat (aliases: meta-llama/Llama-2-13b)` +- `meta-llama/llama-3-1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)` +- `meta-llama/llama-3-1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)` +- `meta-llama/llama-3-2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)` +- `meta-llama/llama-3-2-1b-instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)` +- `meta-llama/llama-3-2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)` +- `meta-llama/llama-3-2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)` +- `meta-llama/llama-guard-3-11b-vision (aliases: meta-llama/Llama-Guard-3-11B-Vision)` + + +### Prerequisite: API Keys + +Make sure you have access to a watsonx API Key. You can get one by referring [watsonx.ai](https://www.ibm.com/docs/en/masv-and-l/maximo-manage/continuous-delivery?topic=setup-create-watsonx-api-key). + + +## Running Llama Stack with watsonx + +You can do this via Conda (build code), venv or Docker which has a pre-built image. + +### Via Docker + +This method allows you to get started quickly without having to build the distribution code. + +```bash +LLAMA_STACK_PORT=5001 +docker run \ + -it \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ./run.yaml:/root/my-run.yaml \ + llamastack/distribution-watsonx \ + --yaml-config /root/my-run.yaml \ + --port $LLAMA_STACK_PORT \ + --env WATSONX_API_KEY=$WATSONX_API_KEY \ + --env WATSONX_PROJECT_ID=$WATSONX_PROJECT_ID \ + --env WATSONX_BASE_URL=$WATSONX_BASE_URL +``` + +### Via Conda + +```bash +llama stack build --template watsonx --image-type conda +llama stack run ./run.yaml \ + --port $LLAMA_STACK_PORT \ + --env WATSONX_API_KEY=$WATSONX_API_KEY \ + --env WATSONX_PROJECT_ID=$WATSONX_PROJECT_ID +``` diff --git a/docs/source/distributions/self_hosted_distro/bedrock.md b/docs/source/distributions/self_hosted_distro/bedrock.md index 302d6932b..d7aedbfb2 100644 --- a/docs/source/distributions/self_hosted_distro/bedrock.md +++ b/docs/source/distributions/self_hosted_distro/bedrock.md @@ -19,7 +19,7 @@ The `llamastack/distribution-bedrock` distribution consists of the following pro | safety | `remote::bedrock` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | diff --git a/docs/source/distributions/self_hosted_distro/cerebras.md b/docs/source/distributions/self_hosted_distro/cerebras.md index 8f441823a..329c9b802 100644 --- a/docs/source/distributions/self_hosted_distro/cerebras.md +++ b/docs/source/distributions/self_hosted_distro/cerebras.md @@ -12,7 +12,7 @@ The `llamastack/distribution-cerebras` distribution consists of the following pr | safety | `inline::llama-guard` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | diff --git a/docs/source/distributions/self_hosted_distro/fireworks.md b/docs/source/distributions/self_hosted_distro/fireworks.md index ee9ddc818..d36e94748 100644 --- a/docs/source/distributions/self_hosted_distro/fireworks.md +++ b/docs/source/distributions/self_hosted_distro/fireworks.md @@ -22,7 +22,7 @@ The `llamastack/distribution-fireworks` distribution consists of the following p | safety | `inline::llama-guard` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::rag-runtime`, `remote::model-context-protocol` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | diff --git a/docs/source/distributions/self_hosted_distro/groq.md b/docs/source/distributions/self_hosted_distro/groq.md index b18be1b2f..1b2194ad8 100644 --- a/docs/source/distributions/self_hosted_distro/groq.md +++ b/docs/source/distributions/self_hosted_distro/groq.md @@ -22,7 +22,7 @@ The `llamastack/distribution-groq` distribution consists of the following provid | safety | `inline::llama-guard` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime` | | vector_io | `inline::faiss` | diff --git a/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md b/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md index b90f75347..8b9dcec55 100644 --- a/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md +++ b/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md @@ -22,7 +22,7 @@ The `llamastack/distribution-meta-reference-gpu` distribution consists of the fo | safety | `inline::llama-guard` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | @@ -81,6 +81,7 @@ LLAMA_STACK_PORT=8321 docker run \ -it \ --pull always \ + --gpu all \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ~/.llama:/root/.llama \ llamastack/distribution-meta-reference-gpu \ @@ -94,6 +95,7 @@ If you are using Llama Stack Safety / Shield APIs, use: docker run \ -it \ --pull always \ + --gpu all \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ~/.llama:/root/.llama \ llamastack/distribution-meta-reference-gpu \ diff --git a/docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md b/docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md deleted file mode 100644 index c3e2b4f2c..000000000 --- a/docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md +++ /dev/null @@ -1,123 +0,0 @@ ---- -orphan: true ---- - -# Meta Reference Quantized Distribution - -```{toctree} -:maxdepth: 2 -:hidden: - -self -``` - -The `llamastack/distribution-meta-reference-quantized-gpu` distribution consists of the following provider configurations: - -| API | Provider(s) | -|-----|-------------| -| agents | `inline::meta-reference` | -| datasetio | `remote::huggingface`, `inline::localfs` | -| eval | `inline::meta-reference` | -| inference | `inline::meta-reference-quantized` | -| safety | `inline::llama-guard` | -| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | -| telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` | -| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | - - -The only difference vs. the `meta-reference-gpu` distribution is that it has support for more efficient inference -- with fp8, int4 quantization, etc. - -Note that you need access to nvidia GPUs to run this distribution. This distribution is not compatible with CPU-only machines or machines with AMD GPUs. - -### Environment Variables - -The following environment variables can be configured: - -- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`) -- `INFERENCE_MODEL`: Inference model loaded into the Meta Reference server (default: `meta-llama/Llama-3.2-3B-Instruct`) -- `INFERENCE_CHECKPOINT_DIR`: Directory containing the Meta Reference model checkpoint (default: `null`) - - -## Prerequisite: Downloading Models - -Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. - -``` -$ llama model list --downloaded -┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ -┃ Model ┃ Size ┃ Modified Time ┃ -┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ -│ Llama3.2-1B-Instruct:int4-qlora-eo8 │ 1.53 GB │ 2025-02-26 11:22:28 │ -├─────────────────────────────────────────┼──────────┼─────────────────────┤ -│ Llama3.2-1B │ 2.31 GB │ 2025-02-18 21:48:52 │ -├─────────────────────────────────────────┼──────────┼─────────────────────┤ -│ Prompt-Guard-86M │ 0.02 GB │ 2025-02-26 11:29:28 │ -├─────────────────────────────────────────┼──────────┼─────────────────────┤ -│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB │ 2025-02-26 11:37:41 │ -├─────────────────────────────────────────┼──────────┼─────────────────────┤ -│ Llama3.2-3B │ 5.99 GB │ 2025-02-18 21:51:26 │ -├─────────────────────────────────────────┼──────────┼─────────────────────┤ -│ Llama3.1-8B │ 14.97 GB │ 2025-02-16 10:36:37 │ -├─────────────────────────────────────────┼──────────┼─────────────────────┤ -│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB │ 2025-02-26 11:35:02 │ -├─────────────────────────────────────────┼──────────┼─────────────────────┤ -│ Llama-Guard-3-1B │ 2.80 GB │ 2025-02-26 11:20:46 │ -├─────────────────────────────────────────┼──────────┼─────────────────────┤ -│ Llama-Guard-3-1B:int4 │ 0.43 GB │ 2025-02-26 11:33:33 │ -└─────────────────────────────────────────┴──────────┴─────────────────────┘ -``` - -## Running the Distribution - -You can do this via Conda (build code) or Docker which has a pre-built image. - -### Via Docker - -This method allows you to get started quickly without having to build the distribution code. - -```bash -LLAMA_STACK_PORT=8321 -docker run \ - -it \ - --pull always \ - -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ - -v ~/.llama:/root/.llama \ - llamastack/distribution-meta-reference-quantized-gpu \ - --port $LLAMA_STACK_PORT \ - --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct -``` - -If you are using Llama Stack Safety / Shield APIs, use: - -```bash -docker run \ - -it \ - --pull always \ - -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ - -v ~/.llama:/root/.llama \ - llamastack/distribution-meta-reference-quantized-gpu \ - --port $LLAMA_STACK_PORT \ - --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \ - --env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B -``` - -### Via Conda - -Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available. - -```bash -llama stack build --template meta-reference-quantized-gpu --image-type conda -llama stack run distributions/meta-reference-quantized-gpu/run.yaml \ - --port $LLAMA_STACK_PORT \ - --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct -``` - -If you are using Llama Stack Safety / Shield APIs, use: - -```bash -llama stack run distributions/meta-reference-quantized-gpu/run-with-safety.yaml \ - --port $LLAMA_STACK_PORT \ - --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \ - --env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B -``` diff --git a/docs/source/distributions/self_hosted_distro/nvidia.md b/docs/source/distributions/self_hosted_distro/nvidia.md index 58731392d..a5bbbfdee 100644 --- a/docs/source/distributions/self_hosted_distro/nvidia.md +++ b/docs/source/distributions/self_hosted_distro/nvidia.md @@ -6,8 +6,8 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov | API | Provider(s) | |-----|-------------| | agents | `inline::meta-reference` | -| datasetio | `inline::localfs` | -| eval | `inline::meta-reference` | +| datasetio | `inline::localfs`, `remote::nvidia` | +| eval | `remote::nvidia` | | inference | `remote::nvidia` | | post_training | `remote::nvidia` | | safety | `remote::nvidia` | @@ -22,13 +22,13 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov The following environment variables can be configured: - `NVIDIA_API_KEY`: NVIDIA API Key (default: ``) -- `NVIDIA_USER_ID`: NVIDIA User ID (default: `llama-stack-user`) +- `NVIDIA_APPEND_API_VERSION`: Whether to append the API version to the base_url (default: `True`) - `NVIDIA_DATASET_NAMESPACE`: NVIDIA Dataset Namespace (default: `default`) -- `NVIDIA_ACCESS_POLICIES`: NVIDIA Access Policies (default: `{}`) - `NVIDIA_PROJECT_ID`: NVIDIA Project ID (default: `test-project`) - `NVIDIA_CUSTOMIZER_URL`: NVIDIA Customizer URL (default: `https://customizer.api.nvidia.com`) - `NVIDIA_OUTPUT_MODEL_DIR`: NVIDIA Output Model Directory (default: `test-example-model@v1`) - `GUARDRAILS_SERVICE_URL`: URL for the NeMo Guardrails Service (default: `http://0.0.0.0:7331`) +- `NVIDIA_EVALUATOR_URL`: URL for the NeMo Evaluator Service (default: `http://0.0.0.0:7331`) - `INFERENCE_MODEL`: Inference model (default: `Llama3.1-8B-Instruct`) - `SAFETY_MODEL`: Name of the model to use for safety (default: `meta/llama-3.1-8b-instruct`) @@ -45,20 +45,91 @@ The following models are available by default: - `meta/llama-3.2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)` - `meta/llama-3.2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)` - `meta/llama-3.2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)` +- `meta/llama-3.3-70b-instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)` - `nvidia/llama-3.2-nv-embedqa-1b-v2 ` - `nvidia/nv-embedqa-e5-v5 ` - `nvidia/nv-embedqa-mistral-7b-v2 ` - `snowflake/arctic-embed-l ` -### Prerequisite: API Keys +## Prerequisites +### NVIDIA API Keys -Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). +Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). Use this key for the `NVIDIA_API_KEY` environment variable. +### Deploy NeMo Microservices Platform +The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for platform prerequisites and instructions to install and deploy the platform. + +## Supported Services +Each Llama Stack API corresponds to a specific NeMo microservice. The core microservices (Customizer, Evaluator, Guardrails) are exposed by the same endpoint. The platform components (Data Store) are each exposed by separate endpoints. + +### Inference: NVIDIA NIM +NVIDIA NIM is used for running inference with registered models. There are two ways to access NVIDIA NIMs: + 1. Hosted (default): Preview APIs hosted at https://integrate.api.nvidia.com (Requires an API key) + 2. Self-hosted: NVIDIA NIMs that run on your own infrastructure. + +The deployed platform includes the NIM Proxy microservice, which is the service that provides to access your NIMs (for example, to run inference on a model). Set the `NVIDIA_BASE_URL` environment variable to use your NVIDIA NIM Proxy deployment. + +### Datasetio API: NeMo Data Store +The NeMo Data Store microservice serves as the default file storage solution for the NeMo microservices platform. It exposts APIs compatible with the Hugging Face Hub client (`HfApi`), so you can use the client to interact with Data Store. The `NVIDIA_DATASETS_URL` environment variable should point to your NeMo Data Store endpoint. + +See the [NVIDIA Datasetio docs](/llama_stack/providers/remote/datasetio/nvidia/README.md) for supported features and example usage. + +### Eval API: NeMo Evaluator +The NeMo Evaluator microservice supports evaluation of LLMs. Launching an Evaluation job with NeMo Evaluator requires an Evaluation Config (an object that contains metadata needed by the job). A Llama Stack Benchmark maps to an Evaluation Config, so registering a Benchmark creates an Evaluation Config in NeMo Evaluator. The `NVIDIA_EVALUATOR_URL` environment variable should point to your NeMo Microservices endpoint. + +See the [NVIDIA Eval docs](/llama_stack/providers/remote/eval/nvidia/README.md) for supported features and example usage. + +### Post-Training API: NeMo Customizer +The NeMo Customizer microservice supports fine-tuning models. You can reference [this list of supported models](/llama_stack/providers/remote/post_training/nvidia/models.py) that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint. + +See the [NVIDIA Post-Training docs](/llama_stack/providers/remote/post_training/nvidia/README.md) for supported features and example usage. + +### Safety API: NeMo Guardrails +The NeMo Guardrails microservice sits between your application and the LLM, and adds checks and content moderation to a model. The `GUARDRAILS_SERVICE_URL` environment variable should point to your NeMo Microservices endpoint. + +See the NVIDIA Safety docs for supported features and example usage. + +## Deploying models +In order to use a registered model with the Llama Stack APIs, ensure the corresponding NIM is deployed to your environment. For example, you can use the NIM Proxy microservice to deploy `meta/llama-3.2-1b-instruct`. + +Note: For improved inference speeds, we need to use NIM with `fast_outlines` guided decoding system (specified in the request body). This is the default if you deployed the platform with the NeMo Microservices Helm Chart. +```sh +# URL to NeMo NIM Proxy service +export NEMO_URL="http://nemo.test" + +curl --location "$NEMO_URL/v1/deployment/model-deployments" \ + -H 'accept: application/json' \ + -H 'Content-Type: application/json' \ + -d '{ + "name": "llama-3.2-1b-instruct", + "namespace": "meta", + "config": { + "model": "meta/llama-3.2-1b-instruct", + "nim_deployment": { + "image_name": "nvcr.io/nim/meta/llama-3.2-1b-instruct", + "image_tag": "1.8.3", + "pvc_size": "25Gi", + "gpu": 1, + "additional_envs": { + "NIM_GUIDED_DECODING_BACKEND": "fast_outlines" + } + } + } + }' +``` +This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/latest/get-started/tutorials/deploy-nims.html) for more information on how to deploy a NIM and verify it's available for inference. + +You can also remove a deployed NIM to free up GPU resources, if needed. +```sh +export NEMO_URL="http://nemo.test" + +curl -X DELETE "$NEMO_URL/v1/deployment/model-deployments/meta/llama-3.1-8b-instruct" +``` ## Running Llama Stack with NVIDIA -You can do this via Conda (build code) or Docker which has a pre-built image. +You can do this via Conda or venv (build code), or Docker which has a pre-built image. ### Via Docker @@ -80,9 +151,23 @@ docker run \ ### Via Conda ```bash +INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct llama stack build --template nvidia --image-type conda llama stack run ./run.yaml \ --port 8321 \ - --env NVIDIA_API_KEY=$NVIDIA_API_KEY + --env NVIDIA_API_KEY=$NVIDIA_API_KEY \ + --env INFERENCE_MODEL=$INFERENCE_MODEL +``` + +### Via venv + +If you've set up your local development environment, you can also build the image using your local virtual environment. + +```bash +INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct +llama stack build --template nvidia --image-type venv +llama stack run ./run.yaml \ + --port 8321 \ + --env NVIDIA_API_KEY=$NVIDIA_API_KEY \ --env INFERENCE_MODEL=$INFERENCE_MODEL ``` diff --git a/docs/source/distributions/self_hosted_distro/ollama.md b/docs/source/distributions/self_hosted_distro/ollama.md index 2358a52a7..5d8935fe2 100644 --- a/docs/source/distributions/self_hosted_distro/ollama.md +++ b/docs/source/distributions/self_hosted_distro/ollama.md @@ -22,7 +22,7 @@ The `llamastack/distribution-ollama` distribution consists of the following prov | safety | `inline::llama-guard` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | diff --git a/docs/source/distributions/self_hosted_distro/passthrough.md b/docs/source/distributions/self_hosted_distro/passthrough.md index 04fc9d927..39f076be4 100644 --- a/docs/source/distributions/self_hosted_distro/passthrough.md +++ b/docs/source/distributions/self_hosted_distro/passthrough.md @@ -22,7 +22,7 @@ The `llamastack/distribution-passthrough` distribution consists of the following | safety | `inline::llama-guard` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::rag-runtime`, `remote::model-context-protocol` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | diff --git a/docs/source/distributions/self_hosted_distro/remote-vllm.md b/docs/source/distributions/self_hosted_distro/remote-vllm.md index e18b5bf40..2ff4bad5b 100644 --- a/docs/source/distributions/self_hosted_distro/remote-vllm.md +++ b/docs/source/distributions/self_hosted_distro/remote-vllm.md @@ -21,7 +21,7 @@ The `llamastack/distribution-remote-vllm` distribution consists of the following | safety | `inline::llama-guard` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | @@ -41,10 +41,10 @@ The following environment variables can be configured: ## Setting up vLLM server -In the following sections, we'll use either AMD and NVIDIA GPUs to serve as hardware accelerators for the vLLM +In the following sections, we'll use AMD, NVIDIA or Intel GPUs to serve as hardware accelerators for the vLLM server, which acts as both the LLM inference provider and the safety provider. Note that vLLM also [supports many other hardware accelerators](https://docs.vllm.ai/en/latest/getting_started/installation.html) and -that we only use GPUs here for demonstration purposes. +that we only use GPUs here for demonstration purposes. Note that if you run into issues, you can include the environment variable `--env VLLM_DEBUG_LOG_API_SERVER_RESPONSE=true` (available in vLLM v0.8.3 and above) in the `docker run` command to enable log response from API server for debugging. ### Setting up vLLM server on AMD GPU @@ -162,6 +162,55 @@ docker run \ --port $SAFETY_PORT ``` +### Setting up vLLM server on Intel GPU + +Refer to [vLLM Documentation for XPU](https://docs.vllm.ai/en/v0.8.2/getting_started/installation/gpu.html?device=xpu) to get a vLLM endpoint. In addition to vLLM side setup which guides towards installing vLLM from sources orself-building vLLM Docker container, Intel provides prebuilt vLLM container to use on systems with Intel GPUs supported by PyTorch XPU backend: +- [intel/vllm](https://hub.docker.com/r/intel/vllm) + +Here is a sample script to start a vLLM server locally via Docker using Intel provided container: + +```bash +export INFERENCE_PORT=8000 +export INFERENCE_MODEL=meta-llama/Llama-3.2-1B-Instruct +export ZE_AFFINITY_MASK=0 + +docker run \ + --pull always \ + --device /dev/dri \ + -v /dev/dri/by-path:/dev/dri/by-path \ + -v ~/.cache/huggingface:/root/.cache/huggingface \ + --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \ + --env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \ + -p $INFERENCE_PORT:$INFERENCE_PORT \ + --ipc=host \ + intel/vllm:xpu \ + --gpu-memory-utilization 0.7 \ + --model $INFERENCE_MODEL \ + --port $INFERENCE_PORT +``` + +If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like: + +```bash +export SAFETY_PORT=8081 +export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B +export ZE_AFFINITY_MASK=1 + +docker run \ + --pull always \ + --device /dev/dri \ + -v /dev/dri/by-path:/dev/dri/by-path \ + -v ~/.cache/huggingface:/root/.cache/huggingface \ + --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \ + --env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \ + -p $SAFETY_PORT:$SAFETY_PORT \ + --ipc=host \ + intel/vllm:xpu \ + --gpu-memory-utilization 0.7 \ + --model $SAFETY_MODEL \ + --port $SAFETY_PORT +``` + ## Running Llama Stack Now you are ready to run Llama Stack with vLLM as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image. diff --git a/docs/source/distributions/self_hosted_distro/sambanova.md b/docs/source/distributions/self_hosted_distro/sambanova.md index 0361c7803..aaa8fd3cc 100644 --- a/docs/source/distributions/self_hosted_distro/sambanova.md +++ b/docs/source/distributions/self_hosted_distro/sambanova.md @@ -19,7 +19,7 @@ The `llamastack/distribution-sambanova` distribution consists of the following p | inference | `remote::sambanova`, `inline::sentence-transformers` | | safety | `inline::llama-guard` | | telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | diff --git a/docs/source/distributions/self_hosted_distro/tgi.md b/docs/source/distributions/self_hosted_distro/tgi.md index f6b14b064..7a75aa559 100644 --- a/docs/source/distributions/self_hosted_distro/tgi.md +++ b/docs/source/distributions/self_hosted_distro/tgi.md @@ -23,7 +23,7 @@ The `llamastack/distribution-tgi` distribution consists of the following provide | safety | `inline::llama-guard` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | diff --git a/docs/source/distributions/self_hosted_distro/together.md b/docs/source/distributions/self_hosted_distro/together.md index 3ebb1f59e..adfc2c472 100644 --- a/docs/source/distributions/self_hosted_distro/together.md +++ b/docs/source/distributions/self_hosted_distro/together.md @@ -22,7 +22,7 @@ The `llamastack/distribution-together` distribution consists of the following pr | safety | `inline::llama-guard` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | diff --git a/docs/source/providers/external.md b/docs/source/providers/external.md index 90fc77979..f9c156dea 100644 --- a/docs/source/providers/external.md +++ b/docs/source/providers/external.md @@ -50,9 +50,11 @@ Llama Stack supports two types of external providers: Here's a list of known external providers that you can use with Llama Stack: -| Type | Name | Description | Repository | -|------|------|-------------|------------| -| Remote | KubeFlow Training | Train models with KubeFlow | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) | +| Name | Description | API | Type | Repository | +|------|-------------|-----|------|------------| +| KubeFlow Training | Train models with KubeFlow | Post Training | Remote | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) | +| KubeFlow Pipelines | Train models with KubeFlow Pipelines | Post Training | Remote | [llama-stack-provider-kfp-trainer](https://github.com/opendatahub-io/llama-stack-provider-kfp-trainer) | +| RamaLama | Inference models with RamaLama | Inference | Remote | [ramalama-stack](https://github.com/containers/ramalama-stack) | ### Remote Provider Specification diff --git a/docs/zero_to_hero_guide/00_Inference101.ipynb b/docs/zero_to_hero_guide/00_Inference101.ipynb index b3b781375..4f71f9f89 100644 --- a/docs/zero_to_hero_guide/00_Inference101.ipynb +++ b/docs/zero_to_hero_guide/00_Inference101.ipynb @@ -389,5 +389,7 @@ "pygments_lexer": "ipython3", "version": "3.10.15" } - } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb b/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb index d66e1b4f5..19a7fe3be 100644 --- a/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb +++ b/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb @@ -256,5 +256,7 @@ "pygments_lexer": "ipython3", "version": "3.10.15" } - } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb b/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb index 7fccf8c51..f3566eeb3 100644 --- a/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb +++ b/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb @@ -301,5 +301,7 @@ "pygments_lexer": "ipython3", "version": "3.12.2" } - } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/zero_to_hero_guide/03_Image_Chat101.ipynb b/docs/zero_to_hero_guide/03_Image_Chat101.ipynb index 58353e813..ae10d8808 100644 --- a/docs/zero_to_hero_guide/03_Image_Chat101.ipynb +++ b/docs/zero_to_hero_guide/03_Image_Chat101.ipynb @@ -200,5 +200,7 @@ "pygments_lexer": "ipython3", "version": "3.12.2" } - } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/zero_to_hero_guide/04_Tool_Calling101.ipynb b/docs/zero_to_hero_guide/04_Tool_Calling101.ipynb index c3a383e8c..de3754b21 100644 --- a/docs/zero_to_hero_guide/04_Tool_Calling101.ipynb +++ b/docs/zero_to_hero_guide/04_Tool_Calling101.ipynb @@ -355,5 +355,7 @@ "pygments_lexer": "ipython3", "version": "3.10.15" } - } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/zero_to_hero_guide/05_Memory101.ipynb b/docs/zero_to_hero_guide/05_Memory101.ipynb index bfeb40adc..66956259f 100644 --- a/docs/zero_to_hero_guide/05_Memory101.ipynb +++ b/docs/zero_to_hero_guide/05_Memory101.ipynb @@ -398,5 +398,7 @@ "pygments_lexer": "ipython3", "version": "3.10.15" } - } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/zero_to_hero_guide/06_Safety101.ipynb b/docs/zero_to_hero_guide/06_Safety101.ipynb index c8c1fe9c7..5d7763924 100644 --- a/docs/zero_to_hero_guide/06_Safety101.ipynb +++ b/docs/zero_to_hero_guide/06_Safety101.ipynb @@ -132,5 +132,7 @@ "pygments_lexer": "ipython3", "version": "3.11.10" } - } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/zero_to_hero_guide/07_Agents101.ipynb b/docs/zero_to_hero_guide/07_Agents101.ipynb index 8c988e1e3..b6df2a4c8 100644 --- a/docs/zero_to_hero_guide/07_Agents101.ipynb +++ b/docs/zero_to_hero_guide/07_Agents101.ipynb @@ -188,5 +188,7 @@ "pygments_lexer": "ipython3", "version": "3.10.15" } - } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/zero_to_hero_guide/README.md b/docs/zero_to_hero_guide/README.md index 9f756de26..96f9768de 100644 --- a/docs/zero_to_hero_guide/README.md +++ b/docs/zero_to_hero_guide/README.md @@ -86,11 +86,11 @@ If you're looking for more specific topics, we have a [Zero to Hero Guide](#next llama stack build --template ollama --image-type conda ``` **Expected Output:** - ``` + ```bash ... - Build Successful! Next steps: - 1. Set the environment variables: LLAMA_STACK_PORT, OLLAMA_URL, INFERENCE_MODEL, SAFETY_MODEL - 2. `llama stack run /Users//.llama/distributions/llamastack-ollama/ollama-run.yaml + Build Successful! + You can find the newly-built template here: ~/.llama/distributions/ollama/ollama-run.yaml + You can run the new Llama Stack Distro via: llama stack run ~/.llama/distributions/ollama/ollama-run.yaml --image-type conda ``` 3. **Set the ENV variables by exporting them to the terminal**: diff --git a/install.sh b/install.sh new file mode 100755 index 000000000..614dbc2f2 --- /dev/null +++ b/install.sh @@ -0,0 +1,145 @@ +#!/usr/bin/env bash +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +set -Eeuo pipefail + +PORT=8321 +OLLAMA_PORT=11434 +MODEL_ALIAS="llama3.2:3b" +SERVER_IMAGE="llamastack/distribution-ollama:0.2.2" +WAIT_TIMEOUT=300 + +log(){ printf "\e[1;32m%s\e[0m\n" "$*"; } +die(){ printf "\e[1;31m❌ %s\e[0m\n" "$*" >&2; exit 1; } + +wait_for_service() { + local url="$1" + local pattern="$2" + local timeout="$3" + local name="$4" + local start ts + log "⏳ Waiting for ${name}…" + start=$(date +%s) + while true; do + if curl --retry 5 --retry-delay 1 --retry-max-time "$timeout" --retry-all-errors --silent --fail "$url" 2>/dev/null | grep -q "$pattern"; then + break + fi + ts=$(date +%s) + if (( ts - start >= timeout )); then + return 1 + fi + printf '.' + sleep 1 + done + return 0 +} + +if command -v docker &> /dev/null; then + ENGINE="docker" +elif command -v podman &> /dev/null; then + ENGINE="podman" +else + die "Docker or Podman is required. Install Docker: https://docs.docker.com/get-docker/ or Podman: https://podman.io/getting-started/installation" +fi + +# Explicitly set the platform for the host architecture +HOST_ARCH="$(uname -m)" +if [ "$HOST_ARCH" = "arm64" ]; then + if [ "$ENGINE" = "docker" ]; then + PLATFORM_OPTS=( --platform linux/amd64 ) + else + PLATFORM_OPTS=( --os linux --arch amd64 ) + fi +else + PLATFORM_OPTS=() +fi + +# macOS + Podman: ensure VM is running before we try to launch containers +# If you need GPU passthrough under Podman on macOS, init the VM with libkrun: +# CONTAINERS_MACHINE_PROVIDER=libkrun podman machine init +if [ "$ENGINE" = "podman" ] && [ "$(uname -s)" = "Darwin" ]; then + if ! podman info &>/dev/null; then + log "⌛️ Initializing Podman VM…" + podman machine init &>/dev/null || true + podman machine start &>/dev/null || true + + log "⌛️ Waiting for Podman API…" + until podman info &>/dev/null; do + sleep 1 + done + log "✅ Podman VM is up" + fi +fi + +# Clean up any leftovers from earlier runs +for name in ollama-server llama-stack; do + ids=$($ENGINE ps -aq --filter "name=^${name}$") + if [ -n "$ids" ]; then + log "⚠️ Found existing container(s) for '${name}', removing…" + $ENGINE rm -f "$ids" > /dev/null 2>&1 + fi +done + +############################################################################### +# 0. Create a shared network +############################################################################### +if ! $ENGINE network inspect llama-net >/dev/null 2>&1; then + log "🌐 Creating network…" + $ENGINE network create llama-net >/dev/null 2>&1 +fi + +############################################################################### +# 1. Ollama +############################################################################### +log "🦙 Starting Ollama…" +$ENGINE run -d "${PLATFORM_OPTS[@]}" --name ollama-server \ + --network llama-net \ + -p "${OLLAMA_PORT}:${OLLAMA_PORT}" \ + ollama/ollama > /dev/null 2>&1 + +if ! wait_for_service "http://localhost:${OLLAMA_PORT}/" "Ollama" "$WAIT_TIMEOUT" "Ollama daemon"; then + log "❌ Ollama daemon did not become ready in ${WAIT_TIMEOUT}s; dumping container logs:" + $ENGINE logs --tail 200 ollama-server + die "Ollama startup failed" +fi + +log "📦 Ensuring model is pulled: ${MODEL_ALIAS}…" +if ! $ENGINE exec ollama-server ollama pull "${MODEL_ALIAS}" > /dev/null 2>&1; then + log "❌ Failed to pull model ${MODEL_ALIAS}; dumping container logs:" + $ENGINE logs --tail 200 ollama-server + die "Model pull failed" +fi + +############################################################################### +# 2. Llama‑Stack +############################################################################### +cmd=( run -d "${PLATFORM_OPTS[@]}" --name llama-stack \ + --network llama-net \ + -p "${PORT}:${PORT}" \ + "${SERVER_IMAGE}" --port "${PORT}" \ + --env INFERENCE_MODEL="${MODEL_ALIAS}" \ + --env OLLAMA_URL="http://ollama-server:${OLLAMA_PORT}" ) + +log "🦙 Starting Llama‑Stack…" +$ENGINE "${cmd[@]}" > /dev/null 2>&1 + +if ! wait_for_service "http://127.0.0.1:${PORT}/v1/health" "OK" "$WAIT_TIMEOUT" "Llama-Stack API"; then + log "❌ Llama-Stack did not become ready in ${WAIT_TIMEOUT}s; dumping container logs:" + $ENGINE logs --tail 200 llama-stack + die "Llama-Stack startup failed" +fi + +############################################################################### +# Done +############################################################################### +log "" +log "🎉 Llama‑Stack is ready!" +log "👉 API endpoint: http://localhost:${PORT}" +log "📖 Documentation: https://llama-stack.readthedocs.io/en/latest/references/index.html" +log "💻 To access the llama‑stack CLI, exec into the container:" +log " $ENGINE exec -ti llama-stack bash" +log "" diff --git a/llama_stack/apis/agents/agents.py b/llama_stack/apis/agents/agents.py index e13c4960b..30b37e98f 100644 --- a/llama_stack/apis/agents/agents.py +++ b/llama_stack/apis/agents/agents.py @@ -4,20 +4,10 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +from collections.abc import AsyncIterator from datetime import datetime from enum import Enum -from typing import ( - Annotated, - Any, - AsyncIterator, - Dict, - List, - Literal, - Optional, - Protocol, - Union, - runtime_checkable, -) +from typing import Annotated, Any, Literal, Protocol, runtime_checkable from pydantic import BaseModel, ConfigDict, Field @@ -38,6 +28,13 @@ from llama_stack.apis.safety import SafetyViolation from llama_stack.apis.tools import ToolDef from llama_stack.schema_utils import json_schema_type, register_schema, webmethod +from .openai_responses import ( + OpenAIResponseInputMessage, + OpenAIResponseInputTool, + OpenAIResponseObject, + OpenAIResponseObjectStream, +) + class Attachment(BaseModel): """An attachment to an agent turn. @@ -72,8 +69,8 @@ class StepCommon(BaseModel): turn_id: str step_id: str - started_at: Optional[datetime] = None - completed_at: Optional[datetime] = None + started_at: datetime | None = None + completed_at: datetime | None = None class StepType(Enum): @@ -113,8 +110,8 @@ class ToolExecutionStep(StepCommon): """ step_type: Literal[StepType.tool_execution.value] = StepType.tool_execution.value - tool_calls: List[ToolCall] - tool_responses: List[ToolResponse] + tool_calls: list[ToolCall] + tool_responses: list[ToolResponse] @json_schema_type @@ -125,7 +122,7 @@ class ShieldCallStep(StepCommon): """ step_type: Literal[StepType.shield_call.value] = StepType.shield_call.value - violation: Optional[SafetyViolation] + violation: SafetyViolation | None @json_schema_type @@ -143,12 +140,7 @@ class MemoryRetrievalStep(StepCommon): Step = Annotated[ - Union[ - InferenceStep, - ToolExecutionStep, - ShieldCallStep, - MemoryRetrievalStep, - ], + InferenceStep | ToolExecutionStep | ShieldCallStep | MemoryRetrievalStep, Field(discriminator="step_type"), ] @@ -159,18 +151,13 @@ class Turn(BaseModel): turn_id: str session_id: str - input_messages: List[ - Union[ - UserMessage, - ToolResponseMessage, - ] - ] - steps: List[Step] + input_messages: list[UserMessage | ToolResponseMessage] + steps: list[Step] output_message: CompletionMessage - output_attachments: Optional[List[Attachment]] = Field(default_factory=list) + output_attachments: list[Attachment] | None = Field(default_factory=list) started_at: datetime - completed_at: Optional[datetime] = None + completed_at: datetime | None = None @json_schema_type @@ -179,34 +166,31 @@ class Session(BaseModel): session_id: str session_name: str - turns: List[Turn] + turns: list[Turn] started_at: datetime class AgentToolGroupWithArgs(BaseModel): name: str - args: Dict[str, Any] + args: dict[str, Any] -AgentToolGroup = Union[ - str, - AgentToolGroupWithArgs, -] +AgentToolGroup = str | AgentToolGroupWithArgs register_schema(AgentToolGroup, name="AgentTool") class AgentConfigCommon(BaseModel): - sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams) + sampling_params: SamplingParams | None = Field(default_factory=SamplingParams) - input_shields: Optional[List[str]] = Field(default_factory=list) - output_shields: Optional[List[str]] = Field(default_factory=list) - toolgroups: Optional[List[AgentToolGroup]] = Field(default_factory=list) - client_tools: Optional[List[ToolDef]] = Field(default_factory=list) - tool_choice: Optional[ToolChoice] = Field(default=None, deprecated="use tool_config instead") - tool_prompt_format: Optional[ToolPromptFormat] = Field(default=None, deprecated="use tool_config instead") - tool_config: Optional[ToolConfig] = Field(default=None) + input_shields: list[str] | None = Field(default_factory=list) + output_shields: list[str] | None = Field(default_factory=list) + toolgroups: list[AgentToolGroup] | None = Field(default_factory=list) + client_tools: list[ToolDef] | None = Field(default_factory=list) + tool_choice: ToolChoice | None = Field(default=None, deprecated="use tool_config instead") + tool_prompt_format: ToolPromptFormat | None = Field(default=None, deprecated="use tool_config instead") + tool_config: ToolConfig | None = Field(default=None) - max_infer_iters: Optional[int] = 10 + max_infer_iters: int | None = 10 def model_post_init(self, __context): if self.tool_config: @@ -225,10 +209,20 @@ class AgentConfigCommon(BaseModel): @json_schema_type class AgentConfig(AgentConfigCommon): + """Configuration for an agent. + + :param model: The model identifier to use for the agent + :param instructions: The system instructions for the agent + :param name: Optional name for the agent, used in telemetry and identification + :param enable_session_persistence: Optional flag indicating whether session data has to be persisted + :param response_format: Optional response format configuration + """ + model: str instructions: str - enable_session_persistence: Optional[bool] = False - response_format: Optional[ResponseFormat] = None + name: str | None = None + enable_session_persistence: bool | None = False + response_format: ResponseFormat | None = None @json_schema_type @@ -240,16 +234,16 @@ class Agent(BaseModel): @json_schema_type class ListAgentsResponse(BaseModel): - data: List[Agent] + data: list[Agent] @json_schema_type class ListAgentSessionsResponse(BaseModel): - data: List[Session] + data: list[Session] class AgentConfigOverridablePerTurn(AgentConfigCommon): - instructions: Optional[str] = None + instructions: str | None = None class AgentTurnResponseEventType(Enum): @@ -267,7 +261,7 @@ class AgentTurnResponseStepStartPayload(BaseModel): event_type: Literal[AgentTurnResponseEventType.step_start.value] = AgentTurnResponseEventType.step_start.value step_type: StepType step_id: str - metadata: Optional[Dict[str, Any]] = Field(default_factory=dict) + metadata: dict[str, Any] | None = Field(default_factory=dict) @json_schema_type @@ -310,14 +304,12 @@ class AgentTurnResponseTurnAwaitingInputPayload(BaseModel): AgentTurnResponseEventPayload = Annotated[ - Union[ - AgentTurnResponseStepStartPayload, - AgentTurnResponseStepProgressPayload, - AgentTurnResponseStepCompletePayload, - AgentTurnResponseTurnStartPayload, - AgentTurnResponseTurnCompletePayload, - AgentTurnResponseTurnAwaitingInputPayload, - ], + AgentTurnResponseStepStartPayload + | AgentTurnResponseStepProgressPayload + | AgentTurnResponseStepCompletePayload + | AgentTurnResponseTurnStartPayload + | AgentTurnResponseTurnCompletePayload + | AgentTurnResponseTurnAwaitingInputPayload, Field(discriminator="event_type"), ] register_schema(AgentTurnResponseEventPayload, name="AgentTurnResponseEventPayload") @@ -346,18 +338,13 @@ class AgentTurnCreateRequest(AgentConfigOverridablePerTurn): # TODO: figure out how we can simplify this and make why # ToolResponseMessage needs to be here (it is function call # execution from outside the system) - messages: List[ - Union[ - UserMessage, - ToolResponseMessage, - ] - ] + messages: list[UserMessage | ToolResponseMessage] - documents: Optional[List[Document]] = None - toolgroups: Optional[List[AgentToolGroup]] = None + documents: list[Document] | None = None + toolgroups: list[AgentToolGroup] | None = None - stream: Optional[bool] = False - tool_config: Optional[ToolConfig] = None + stream: bool | None = False + tool_config: ToolConfig | None = None @json_schema_type @@ -365,8 +352,8 @@ class AgentTurnResumeRequest(BaseModel): agent_id: str session_id: str turn_id: str - tool_responses: List[ToolResponse] - stream: Optional[bool] = False + tool_responses: list[ToolResponse] + stream: bool | None = False @json_schema_type @@ -412,17 +399,12 @@ class Agents(Protocol): self, agent_id: str, session_id: str, - messages: List[ - Union[ - UserMessage, - ToolResponseMessage, - ] - ], - stream: Optional[bool] = False, - documents: Optional[List[Document]] = None, - toolgroups: Optional[List[AgentToolGroup]] = None, - tool_config: Optional[ToolConfig] = None, - ) -> Union[Turn, AsyncIterator[AgentTurnResponseStreamChunk]]: + messages: list[UserMessage | ToolResponseMessage], + stream: bool | None = False, + documents: list[Document] | None = None, + toolgroups: list[AgentToolGroup] | None = None, + tool_config: ToolConfig | None = None, + ) -> Turn | AsyncIterator[AgentTurnResponseStreamChunk]: """Create a new turn for an agent. :param agent_id: The ID of the agent to create the turn for. @@ -446,9 +428,9 @@ class Agents(Protocol): agent_id: str, session_id: str, turn_id: str, - tool_responses: List[ToolResponse], - stream: Optional[bool] = False, - ) -> Union[Turn, AsyncIterator[AgentTurnResponseStreamChunk]]: + tool_responses: list[ToolResponse], + stream: bool | None = False, + ) -> Turn | AsyncIterator[AgentTurnResponseStreamChunk]: """Resume an agent turn with executed tool call responses. When a Turn has the status `awaiting_input` due to pending input from client side tool calls, this endpoint can be used to submit the outputs from the tool calls once they are ready. @@ -521,7 +503,7 @@ class Agents(Protocol): self, session_id: str, agent_id: str, - turn_ids: Optional[List[str]] = None, + turn_ids: list[str] | None = None, ) -> Session: """Retrieve an agent session by its ID. @@ -583,3 +565,40 @@ class Agents(Protocol): :returns: A ListAgentSessionsResponse. """ ... + + # We situate the OpenAI Responses API in the Agents API just like we did things + # for Inference. The Responses API, in its intent, serves the same purpose as + # the Agents API above -- it is essentially a lightweight "agentic loop" with + # integrated tool calling. + # + # Both of these APIs are inherently stateful. + + @webmethod(route="/openai/v1/responses/{id}", method="GET") + async def get_openai_response( + self, + id: str, + ) -> OpenAIResponseObject: + """Retrieve an OpenAI response by its ID. + + :param id: The ID of the OpenAI response to retrieve. + :returns: An OpenAIResponseObject. + """ + ... + + @webmethod(route="/openai/v1/responses", method="POST") + async def create_openai_response( + self, + input: str | list[OpenAIResponseInputMessage], + model: str, + previous_response_id: str | None = None, + store: bool | None = True, + stream: bool | None = False, + temperature: float | None = None, + tools: list[OpenAIResponseInputTool] | None = None, + ) -> OpenAIResponseObject | AsyncIterator[OpenAIResponseObjectStream]: + """Create a new OpenAI response. + + :param input: Input message(s) to create the response. + :param model: The underlying LLM used for completions. + :param previous_response_id: (Optional) if specified, the new response will be a continuation of the previous response. This can be used to easily fork-off new responses from existing responses. + """ diff --git a/llama_stack/apis/agents/openai_responses.py b/llama_stack/apis/agents/openai_responses.py new file mode 100644 index 000000000..8e11b2123 --- /dev/null +++ b/llama_stack/apis/agents/openai_responses.py @@ -0,0 +1,133 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Annotated, Literal + +from pydantic import BaseModel, Field + +from llama_stack.schema_utils import json_schema_type, register_schema + + +@json_schema_type +class OpenAIResponseError(BaseModel): + code: str + message: str + + +@json_schema_type +class OpenAIResponseOutputMessageContentOutputText(BaseModel): + text: str + type: Literal["output_text"] = "output_text" + + +OpenAIResponseOutputMessageContent = Annotated[ + OpenAIResponseOutputMessageContentOutputText, + Field(discriminator="type"), +] +register_schema(OpenAIResponseOutputMessageContent, name="OpenAIResponseOutputMessageContent") + + +@json_schema_type +class OpenAIResponseOutputMessage(BaseModel): + id: str + content: list[OpenAIResponseOutputMessageContent] + role: Literal["assistant"] = "assistant" + status: str + type: Literal["message"] = "message" + + +@json_schema_type +class OpenAIResponseOutputMessageWebSearchToolCall(BaseModel): + id: str + status: str + type: Literal["web_search_call"] = "web_search_call" + + +OpenAIResponseOutput = Annotated[ + OpenAIResponseOutputMessage | OpenAIResponseOutputMessageWebSearchToolCall, + Field(discriminator="type"), +] +register_schema(OpenAIResponseOutput, name="OpenAIResponseOutput") + + +@json_schema_type +class OpenAIResponseObject(BaseModel): + created_at: int + error: OpenAIResponseError | None = None + id: str + model: str + object: Literal["response"] = "response" + output: list[OpenAIResponseOutput] + parallel_tool_calls: bool = False + previous_response_id: str | None = None + status: str + temperature: float | None = None + top_p: float | None = None + truncation: str | None = None + user: str | None = None + + +@json_schema_type +class OpenAIResponseObjectStreamResponseCreated(BaseModel): + response: OpenAIResponseObject + type: Literal["response.created"] = "response.created" + + +@json_schema_type +class OpenAIResponseObjectStreamResponseCompleted(BaseModel): + response: OpenAIResponseObject + type: Literal["response.completed"] = "response.completed" + + +OpenAIResponseObjectStream = Annotated[ + OpenAIResponseObjectStreamResponseCreated | OpenAIResponseObjectStreamResponseCompleted, + Field(discriminator="type"), +] +register_schema(OpenAIResponseObjectStream, name="OpenAIResponseObjectStream") + + +@json_schema_type +class OpenAIResponseInputMessageContentText(BaseModel): + text: str + type: Literal["input_text"] = "input_text" + + +@json_schema_type +class OpenAIResponseInputMessageContentImage(BaseModel): + detail: Literal["low"] | Literal["high"] | Literal["auto"] = "auto" + type: Literal["input_image"] = "input_image" + # TODO: handle file_id + image_url: str | None = None + + +# TODO: handle file content types +OpenAIResponseInputMessageContent = Annotated[ + OpenAIResponseInputMessageContentText | OpenAIResponseInputMessageContentImage, + Field(discriminator="type"), +] +register_schema(OpenAIResponseInputMessageContent, name="OpenAIResponseInputMessageContent") + + +@json_schema_type +class OpenAIResponseInputMessage(BaseModel): + content: str | list[OpenAIResponseInputMessageContent] + role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"] + type: Literal["message"] | None = "message" + + +@json_schema_type +class OpenAIResponseInputToolWebSearch(BaseModel): + type: Literal["web_search"] | Literal["web_search_preview_2025_03_11"] = "web_search" + # TODO: actually use search_context_size somewhere... + search_context_size: str | None = Field(default="medium", pattern="^low|medium|high$") + # TODO: add user_location + + +OpenAIResponseInputTool = Annotated[ + OpenAIResponseInputToolWebSearch, + Field(discriminator="type"), +] +register_schema(OpenAIResponseInputTool, name="OpenAIResponseInputTool") diff --git a/llama_stack/apis/batch_inference/batch_inference.py b/llama_stack/apis/batch_inference/batch_inference.py index 7a324128d..79bc73e4c 100644 --- a/llama_stack/apis/batch_inference/batch_inference.py +++ b/llama_stack/apis/batch_inference/batch_inference.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List, Optional, Protocol, runtime_checkable +from typing import Protocol, runtime_checkable from llama_stack.apis.common.job_types import Job from llama_stack.apis.inference import ( @@ -34,22 +34,22 @@ class BatchInference(Protocol): async def completion( self, model: str, - content_batch: List[InterleavedContent], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + content_batch: list[InterleavedContent], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ) -> Job: ... @webmethod(route="/batch-inference/chat-completion", method="POST") async def chat_completion( self, model: str, - messages_batch: List[List[Message]], - sampling_params: Optional[SamplingParams] = None, + messages_batch: list[list[Message]], + sampling_params: SamplingParams | None = None, # zero-shot tool definitions as input to the model - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ) -> Job: ... diff --git a/llama_stack/apis/benchmarks/benchmarks.py b/llama_stack/apis/benchmarks/benchmarks.py index 809af8868..1bba42d20 100644 --- a/llama_stack/apis/benchmarks/benchmarks.py +++ b/llama_stack/apis/benchmarks/benchmarks.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Literal, Optional, Protocol, runtime_checkable +from typing import Any, Literal, Protocol, runtime_checkable from pydantic import BaseModel, Field @@ -13,8 +13,8 @@ from llama_stack.schema_utils import json_schema_type, webmethod class CommonBenchmarkFields(BaseModel): dataset_id: str - scoring_functions: List[str] - metadata: Dict[str, Any] = Field( + scoring_functions: list[str] + metadata: dict[str, Any] = Field( default_factory=dict, description="Metadata for this evaluation task", ) @@ -35,12 +35,12 @@ class Benchmark(CommonBenchmarkFields, Resource): class BenchmarkInput(CommonBenchmarkFields, BaseModel): benchmark_id: str - provider_id: Optional[str] = None - provider_benchmark_id: Optional[str] = None + provider_id: str | None = None + provider_benchmark_id: str | None = None class ListBenchmarksResponse(BaseModel): - data: List[Benchmark] + data: list[Benchmark] @runtime_checkable @@ -59,8 +59,8 @@ class Benchmarks(Protocol): self, benchmark_id: str, dataset_id: str, - scoring_functions: List[str], - provider_benchmark_id: Optional[str] = None, - provider_id: Optional[str] = None, - metadata: Optional[Dict[str, Any]] = None, + scoring_functions: list[str], + provider_benchmark_id: str | None = None, + provider_id: str | None = None, + metadata: dict[str, Any] | None = None, ) -> None: ... diff --git a/llama_stack/apis/common/content_types.py b/llama_stack/apis/common/content_types.py index 9d4e21308..b9ef033dd 100644 --- a/llama_stack/apis/common/content_types.py +++ b/llama_stack/apis/common/content_types.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Annotated, List, Literal, Optional, Union +from typing import Annotated, Literal from pydantic import BaseModel, Field, model_validator @@ -26,9 +26,9 @@ class _URLOrData(BaseModel): :param data: base64 encoded image data as string """ - url: Optional[URL] = None + url: URL | None = None # data is a base64 encoded string, hint with contentEncoding=base64 - data: Optional[str] = Field(contentEncoding="base64", default=None) + data: str | None = Field(contentEncoding="base64", default=None) @model_validator(mode="before") @classmethod @@ -64,13 +64,13 @@ class TextContentItem(BaseModel): # other modalities can be added here InterleavedContentItem = Annotated[ - Union[ImageContentItem, TextContentItem], + ImageContentItem | TextContentItem, Field(discriminator="type"), ] register_schema(InterleavedContentItem, name="InterleavedContentItem") # accept a single "str" as a special case since it is common -InterleavedContent = Union[str, InterleavedContentItem, List[InterleavedContentItem]] +InterleavedContent = str | InterleavedContentItem | list[InterleavedContentItem] register_schema(InterleavedContent, name="InterleavedContent") @@ -100,13 +100,13 @@ class ToolCallDelta(BaseModel): # you either send an in-progress tool call so the client can stream a long # code generation or you send the final parsed tool call at the end of the # stream - tool_call: Union[str, ToolCall] + tool_call: str | ToolCall parse_status: ToolCallParseStatus # streaming completions send a stream of ContentDeltas ContentDelta = Annotated[ - Union[TextDelta, ImageDelta, ToolCallDelta], + TextDelta | ImageDelta | ToolCallDelta, Field(discriminator="type"), ] register_schema(ContentDelta, name="ContentDelta") diff --git a/llama_stack/apis/common/deployment_types.py b/llama_stack/apis/common/deployment_types.py index 83eea28a2..4d01d7ad1 100644 --- a/llama_stack/apis/common/deployment_types.py +++ b/llama_stack/apis/common/deployment_types.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel @@ -25,6 +25,6 @@ class RestAPIMethod(Enum): class RestAPIExecutionConfig(BaseModel): url: URL method: RestAPIMethod - params: Optional[Dict[str, Any]] = None - headers: Optional[Dict[str, Any]] = None - body: Optional[Dict[str, Any]] = None + params: dict[str, Any] | None = None + headers: dict[str, Any] | None = None + body: dict[str, Any] | None = None diff --git a/llama_stack/apis/common/responses.py b/llama_stack/apis/common/responses.py index f9e9a4c31..b3bb5cb6b 100644 --- a/llama_stack/apis/common/responses.py +++ b/llama_stack/apis/common/responses.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List +from typing import Any from pydantic import BaseModel @@ -19,5 +19,5 @@ class PaginatedResponse(BaseModel): :param has_more: Whether there are more items available after this set """ - data: List[Dict[str, Any]] + data: list[dict[str, Any]] has_more: bool diff --git a/llama_stack/apis/common/training_types.py b/llama_stack/apis/common/training_types.py index d6c6c6919..46cd101af 100644 --- a/llama_stack/apis/common/training_types.py +++ b/llama_stack/apis/common/training_types.py @@ -5,7 +5,6 @@ # the root directory of this source tree. from datetime import datetime -from typing import Optional from pydantic import BaseModel @@ -27,4 +26,4 @@ class Checkpoint(BaseModel): epoch: int post_training_job_id: str path: str - training_metrics: Optional[PostTrainingMetric] = None + training_metrics: PostTrainingMetric | None = None diff --git a/llama_stack/apis/common/type_system.py b/llama_stack/apis/common/type_system.py index 5d9f000be..db4aab4c5 100644 --- a/llama_stack/apis/common/type_system.py +++ b/llama_stack/apis/common/type_system.py @@ -4,10 +4,9 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Literal, Union +from typing import Annotated, Literal from pydantic import BaseModel, Field -from typing_extensions import Annotated from llama_stack.schema_utils import json_schema_type, register_schema @@ -73,18 +72,16 @@ class DialogType(BaseModel): ParamType = Annotated[ - Union[ - StringType, - NumberType, - BooleanType, - ArrayType, - ObjectType, - JsonType, - UnionType, - ChatCompletionInputType, - CompletionInputType, - AgentTurnInputType, - ], + StringType + | NumberType + | BooleanType + | ArrayType + | ObjectType + | JsonType + | UnionType + | ChatCompletionInputType + | CompletionInputType + | AgentTurnInputType, Field(discriminator="type"), ] register_schema(ParamType, name="ParamType") diff --git a/llama_stack/apis/datasetio/datasetio.py b/llama_stack/apis/datasetio/datasetio.py index 6331882fb..6d160a043 100644 --- a/llama_stack/apis/datasetio/datasetio.py +++ b/llama_stack/apis/datasetio/datasetio.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Optional, Protocol, runtime_checkable +from typing import Any, Protocol, runtime_checkable from llama_stack.apis.common.responses import PaginatedResponse from llama_stack.apis.datasets import Dataset @@ -24,8 +24,8 @@ class DatasetIO(Protocol): async def iterrows( self, dataset_id: str, - start_index: Optional[int] = None, - limit: Optional[int] = None, + start_index: int | None = None, + limit: int | None = None, ) -> PaginatedResponse: """Get a paginated list of rows from a dataset. @@ -44,4 +44,4 @@ class DatasetIO(Protocol): ... @webmethod(route="/datasetio/append-rows/{dataset_id:path}", method="POST") - async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None: ... + async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None: ... diff --git a/llama_stack/apis/datasets/datasets.py b/llama_stack/apis/datasets/datasets.py index 32ccde144..796217557 100644 --- a/llama_stack/apis/datasets/datasets.py +++ b/llama_stack/apis/datasets/datasets.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Annotated, Any, Dict, List, Literal, Optional, Protocol, Union +from typing import Annotated, Any, Literal, Protocol from pydantic import BaseModel, Field @@ -81,11 +81,11 @@ class RowsDataSource(BaseModel): """ type: Literal["rows"] = "rows" - rows: List[Dict[str, Any]] + rows: list[dict[str, Any]] DataSource = Annotated[ - Union[URIDataSource, RowsDataSource], + URIDataSource | RowsDataSource, Field(discriminator="type"), ] register_schema(DataSource, name="DataSource") @@ -98,7 +98,7 @@ class CommonDatasetFields(BaseModel): purpose: DatasetPurpose source: DataSource - metadata: Dict[str, Any] = Field( + metadata: dict[str, Any] = Field( default_factory=dict, description="Any additional metadata for this dataset", ) @@ -122,7 +122,7 @@ class DatasetInput(CommonDatasetFields, BaseModel): class ListDatasetsResponse(BaseModel): - data: List[Dataset] + data: list[Dataset] class Datasets(Protocol): @@ -131,8 +131,8 @@ class Datasets(Protocol): self, purpose: DatasetPurpose, source: DataSource, - metadata: Optional[Dict[str, Any]] = None, - dataset_id: Optional[str] = None, + metadata: dict[str, Any] | None = None, + dataset_id: str | None = None, ) -> Dataset: """ Register a new dataset. diff --git a/llama_stack/apis/datatypes.py b/llama_stack/apis/datatypes.py index 25f3ab1ab..63a764725 100644 --- a/llama_stack/apis/datatypes.py +++ b/llama_stack/apis/datatypes.py @@ -5,7 +5,6 @@ # the root directory of this source tree. from enum import Enum -from typing import Optional from pydantic import BaseModel @@ -54,4 +53,4 @@ class Error(BaseModel): status: int title: str detail: str - instance: Optional[str] = None + instance: str | None = None diff --git a/llama_stack/apis/eval/eval.py b/llama_stack/apis/eval/eval.py index 0e5959c37..23ca89a94 100644 --- a/llama_stack/apis/eval/eval.py +++ b/llama_stack/apis/eval/eval.py @@ -4,10 +4,9 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Literal, Optional, Protocol, Union +from typing import Annotated, Any, Literal, Protocol from pydantic import BaseModel, Field -from typing_extensions import Annotated from llama_stack.apis.agents import AgentConfig from llama_stack.apis.common.job_types import Job @@ -29,7 +28,7 @@ class ModelCandidate(BaseModel): type: Literal["model"] = "model" model: str sampling_params: SamplingParams - system_message: Optional[SystemMessage] = None + system_message: SystemMessage | None = None @json_schema_type @@ -43,7 +42,7 @@ class AgentCandidate(BaseModel): config: AgentConfig -EvalCandidate = Annotated[Union[ModelCandidate, AgentCandidate], Field(discriminator="type")] +EvalCandidate = Annotated[ModelCandidate | AgentCandidate, Field(discriminator="type")] register_schema(EvalCandidate, name="EvalCandidate") @@ -57,11 +56,11 @@ class BenchmarkConfig(BaseModel): """ eval_candidate: EvalCandidate - scoring_params: Dict[str, ScoringFnParams] = Field( + scoring_params: dict[str, ScoringFnParams] = Field( description="Map between scoring function id and parameters for each scoring function you want to run", default_factory=dict, ) - num_examples: Optional[int] = Field( + num_examples: int | None = Field( description="Number of examples to evaluate (useful for testing), if not provided, all examples in the dataset will be evaluated", default=None, ) @@ -76,9 +75,9 @@ class EvaluateResponse(BaseModel): :param scores: The scores from the evaluation. """ - generations: List[Dict[str, Any]] + generations: list[dict[str, Any]] # each key in the dict is a scoring function name - scores: Dict[str, ScoringResult] + scores: dict[str, ScoringResult] class Eval(Protocol): @@ -101,8 +100,8 @@ class Eval(Protocol): async def evaluate_rows( self, benchmark_id: str, - input_rows: List[Dict[str, Any]], - scoring_functions: List[str], + input_rows: list[dict[str, Any]], + scoring_functions: list[str], benchmark_config: BenchmarkConfig, ) -> EvaluateResponse: """Evaluate a list of rows on a benchmark. diff --git a/llama_stack/apis/files/files.py b/llama_stack/apis/files/files.py index ef8b65829..4a9b49978 100644 --- a/llama_stack/apis/files/files.py +++ b/llama_stack/apis/files/files.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List, Optional, Protocol, runtime_checkable +from typing import Protocol, runtime_checkable from pydantic import BaseModel @@ -42,7 +42,7 @@ class ListBucketResponse(BaseModel): :param data: List of FileResponse entries """ - data: List[BucketResponse] + data: list[BucketResponse] @json_schema_type @@ -74,7 +74,7 @@ class ListFileResponse(BaseModel): :param data: List of FileResponse entries """ - data: List[FileResponse] + data: list[FileResponse] @runtime_checkable @@ -102,7 +102,7 @@ class Files(Protocol): async def upload_content_to_session( self, upload_id: str, - ) -> Optional[FileResponse]: + ) -> FileResponse | None: """ Upload file content to an existing upload session. On the server, request body will have the raw bytes that are uploaded. diff --git a/llama_stack/apis/inference/inference.py b/llama_stack/apis/inference/inference.py index 596efb136..dbcd1d019 100644 --- a/llama_stack/apis/inference/inference.py +++ b/llama_stack/apis/inference/inference.py @@ -4,21 +4,18 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +from collections.abc import AsyncIterator from enum import Enum from typing import ( + Annotated, Any, - AsyncIterator, - Dict, - List, Literal, - Optional, Protocol, - Union, runtime_checkable, ) from pydantic import BaseModel, Field, field_validator -from typing_extensions import Annotated, TypedDict +from typing_extensions import TypedDict from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent, InterleavedContentItem from llama_stack.apis.models import Model @@ -47,8 +44,8 @@ class GreedySamplingStrategy(BaseModel): @json_schema_type class TopPSamplingStrategy(BaseModel): type: Literal["top_p"] = "top_p" - temperature: Optional[float] = Field(..., gt=0.0) - top_p: Optional[float] = 0.95 + temperature: float | None = Field(..., gt=0.0) + top_p: float | None = 0.95 @json_schema_type @@ -58,7 +55,7 @@ class TopKSamplingStrategy(BaseModel): SamplingStrategy = Annotated[ - Union[GreedySamplingStrategy, TopPSamplingStrategy, TopKSamplingStrategy], + GreedySamplingStrategy | TopPSamplingStrategy | TopKSamplingStrategy, Field(discriminator="type"), ] register_schema(SamplingStrategy, name="SamplingStrategy") @@ -79,9 +76,9 @@ class SamplingParams(BaseModel): strategy: SamplingStrategy = Field(default_factory=GreedySamplingStrategy) - max_tokens: Optional[int] = 0 - repetition_penalty: Optional[float] = 1.0 - stop: Optional[List[str]] = None + max_tokens: int | None = 0 + repetition_penalty: float | None = 1.0 + stop: list[str] | None = None class LogProbConfig(BaseModel): @@ -90,7 +87,7 @@ class LogProbConfig(BaseModel): :param top_k: How many tokens (for each position) to return log probabilities for. """ - top_k: Optional[int] = 0 + top_k: int | None = 0 class QuantizationType(Enum): @@ -125,11 +122,11 @@ class Int4QuantizationConfig(BaseModel): """ type: Literal["int4_mixed"] = "int4_mixed" - scheme: Optional[str] = "int4_weight_int8_dynamic_activation" + scheme: str | None = "int4_weight_int8_dynamic_activation" QuantizationConfig = Annotated[ - Union[Bf16QuantizationConfig, Fp8QuantizationConfig, Int4QuantizationConfig], + Bf16QuantizationConfig | Fp8QuantizationConfig | Int4QuantizationConfig, Field(discriminator="type"), ] @@ -145,7 +142,7 @@ class UserMessage(BaseModel): role: Literal["user"] = "user" content: InterleavedContent - context: Optional[InterleavedContent] = None + context: InterleavedContent | None = None @json_schema_type @@ -190,16 +187,11 @@ class CompletionMessage(BaseModel): role: Literal["assistant"] = "assistant" content: InterleavedContent stop_reason: StopReason - tool_calls: Optional[List[ToolCall]] = Field(default_factory=list) + tool_calls: list[ToolCall] | None = Field(default_factory=list) Message = Annotated[ - Union[ - UserMessage, - SystemMessage, - ToolResponseMessage, - CompletionMessage, - ], + UserMessage | SystemMessage | ToolResponseMessage | CompletionMessage, Field(discriminator="role"), ] register_schema(Message, name="Message") @@ -208,9 +200,9 @@ register_schema(Message, name="Message") @json_schema_type class ToolResponse(BaseModel): call_id: str - tool_name: Union[BuiltinTool, str] + tool_name: BuiltinTool | str content: InterleavedContent - metadata: Optional[Dict[str, Any]] = None + metadata: dict[str, Any] | None = None @field_validator("tool_name", mode="before") @classmethod @@ -243,7 +235,7 @@ class TokenLogProbs(BaseModel): :param logprobs_by_token: Dictionary mapping tokens to their log probabilities """ - logprobs_by_token: Dict[str, float] + logprobs_by_token: dict[str, float] class ChatCompletionResponseEventType(Enum): @@ -271,8 +263,8 @@ class ChatCompletionResponseEvent(BaseModel): event_type: ChatCompletionResponseEventType delta: ContentDelta - logprobs: Optional[List[TokenLogProbs]] = None - stop_reason: Optional[StopReason] = None + logprobs: list[TokenLogProbs] | None = None + stop_reason: StopReason | None = None class ResponseFormatType(Enum): @@ -295,7 +287,7 @@ class JsonSchemaResponseFormat(BaseModel): """ type: Literal[ResponseFormatType.json_schema.value] = ResponseFormatType.json_schema.value - json_schema: Dict[str, Any] + json_schema: dict[str, Any] @json_schema_type @@ -307,11 +299,11 @@ class GrammarResponseFormat(BaseModel): """ type: Literal[ResponseFormatType.grammar.value] = ResponseFormatType.grammar.value - bnf: Dict[str, Any] + bnf: dict[str, Any] ResponseFormat = Annotated[ - Union[JsonSchemaResponseFormat, GrammarResponseFormat], + JsonSchemaResponseFormat | GrammarResponseFormat, Field(discriminator="type"), ] register_schema(ResponseFormat, name="ResponseFormat") @@ -321,10 +313,10 @@ register_schema(ResponseFormat, name="ResponseFormat") class CompletionRequest(BaseModel): model: str content: InterleavedContent - sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams) - response_format: Optional[ResponseFormat] = None - stream: Optional[bool] = False - logprobs: Optional[LogProbConfig] = None + sampling_params: SamplingParams | None = Field(default_factory=SamplingParams) + response_format: ResponseFormat | None = None + stream: bool | None = False + logprobs: LogProbConfig | None = None @json_schema_type @@ -338,7 +330,7 @@ class CompletionResponse(MetricResponseMixin): content: str stop_reason: StopReason - logprobs: Optional[List[TokenLogProbs]] = None + logprobs: list[TokenLogProbs] | None = None @json_schema_type @@ -351,8 +343,8 @@ class CompletionResponseStreamChunk(MetricResponseMixin): """ delta: str - stop_reason: Optional[StopReason] = None - logprobs: Optional[List[TokenLogProbs]] = None + stop_reason: StopReason | None = None + logprobs: list[TokenLogProbs] | None = None class SystemMessageBehavior(Enum): @@ -383,9 +375,9 @@ class ToolConfig(BaseModel): '{{function_definitions}}' to indicate where the function definitions should be inserted. """ - tool_choice: Optional[ToolChoice | str] = Field(default=ToolChoice.auto) - tool_prompt_format: Optional[ToolPromptFormat] = Field(default=None) - system_message_behavior: Optional[SystemMessageBehavior] = Field(default=SystemMessageBehavior.append) + tool_choice: ToolChoice | str | None = Field(default=ToolChoice.auto) + tool_prompt_format: ToolPromptFormat | None = Field(default=None) + system_message_behavior: SystemMessageBehavior | None = Field(default=SystemMessageBehavior.append) def model_post_init(self, __context: Any) -> None: if isinstance(self.tool_choice, str): @@ -399,15 +391,15 @@ class ToolConfig(BaseModel): @json_schema_type class ChatCompletionRequest(BaseModel): model: str - messages: List[Message] - sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams) + messages: list[Message] + sampling_params: SamplingParams | None = Field(default_factory=SamplingParams) - tools: Optional[List[ToolDefinition]] = Field(default_factory=list) - tool_config: Optional[ToolConfig] = Field(default_factory=ToolConfig) + tools: list[ToolDefinition] | None = Field(default_factory=list) + tool_config: ToolConfig | None = Field(default_factory=ToolConfig) - response_format: Optional[ResponseFormat] = None - stream: Optional[bool] = False - logprobs: Optional[LogProbConfig] = None + response_format: ResponseFormat | None = None + stream: bool | None = False + logprobs: LogProbConfig | None = None @json_schema_type @@ -429,7 +421,7 @@ class ChatCompletionResponse(MetricResponseMixin): """ completion_message: CompletionMessage - logprobs: Optional[List[TokenLogProbs]] = None + logprobs: list[TokenLogProbs] | None = None @json_schema_type @@ -439,7 +431,7 @@ class EmbeddingsResponse(BaseModel): :param embeddings: List of embedding vectors, one per input content. Each embedding is a list of floats. The dimensionality of the embedding is model-specific; you can check model metadata using /models/{model_id} """ - embeddings: List[List[float]] + embeddings: list[list[float]] @json_schema_type @@ -451,7 +443,7 @@ class OpenAIChatCompletionContentPartTextParam(BaseModel): @json_schema_type class OpenAIImageURL(BaseModel): url: str - detail: Optional[str] = None + detail: str | None = None @json_schema_type @@ -461,16 +453,13 @@ class OpenAIChatCompletionContentPartImageParam(BaseModel): OpenAIChatCompletionContentPartParam = Annotated[ - Union[ - OpenAIChatCompletionContentPartTextParam, - OpenAIChatCompletionContentPartImageParam, - ], + OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam, Field(discriminator="type"), ] register_schema(OpenAIChatCompletionContentPartParam, name="OpenAIChatCompletionContentPartParam") -OpenAIChatCompletionMessageContent = Union[str, List[OpenAIChatCompletionContentPartParam]] +OpenAIChatCompletionMessageContent = str | list[OpenAIChatCompletionContentPartParam] @json_schema_type @@ -484,7 +473,7 @@ class OpenAIUserMessageParam(BaseModel): role: Literal["user"] = "user" content: OpenAIChatCompletionMessageContent - name: Optional[str] = None + name: str | None = None @json_schema_type @@ -498,21 +487,21 @@ class OpenAISystemMessageParam(BaseModel): role: Literal["system"] = "system" content: OpenAIChatCompletionMessageContent - name: Optional[str] = None + name: str | None = None @json_schema_type class OpenAIChatCompletionToolCallFunction(BaseModel): - name: Optional[str] = None - arguments: Optional[str] = None + name: str | None = None + arguments: str | None = None @json_schema_type class OpenAIChatCompletionToolCall(BaseModel): - index: Optional[int] = None - id: Optional[str] = None + index: int | None = None + id: str | None = None type: Literal["function"] = "function" - function: Optional[OpenAIChatCompletionToolCallFunction] = None + function: OpenAIChatCompletionToolCallFunction | None = None @json_schema_type @@ -526,9 +515,9 @@ class OpenAIAssistantMessageParam(BaseModel): """ role: Literal["assistant"] = "assistant" - content: OpenAIChatCompletionMessageContent - name: Optional[str] = None - tool_calls: Optional[List[OpenAIChatCompletionToolCall]] = Field(default_factory=list) + content: OpenAIChatCompletionMessageContent | None = None + name: str | None = None + tool_calls: list[OpenAIChatCompletionToolCall] | None = None @json_schema_type @@ -556,17 +545,15 @@ class OpenAIDeveloperMessageParam(BaseModel): role: Literal["developer"] = "developer" content: OpenAIChatCompletionMessageContent - name: Optional[str] = None + name: str | None = None OpenAIMessageParam = Annotated[ - Union[ - OpenAIUserMessageParam, - OpenAISystemMessageParam, - OpenAIAssistantMessageParam, - OpenAIToolMessageParam, - OpenAIDeveloperMessageParam, - ], + OpenAIUserMessageParam + | OpenAISystemMessageParam + | OpenAIAssistantMessageParam + | OpenAIToolMessageParam + | OpenAIDeveloperMessageParam, Field(discriminator="role"), ] register_schema(OpenAIMessageParam, name="OpenAIMessageParam") @@ -580,14 +567,14 @@ class OpenAIResponseFormatText(BaseModel): @json_schema_type class OpenAIJSONSchema(TypedDict, total=False): name: str - description: Optional[str] = None - strict: Optional[bool] = None + description: str | None = None + strict: bool | None = None # Pydantic BaseModel cannot be used with a schema param, since it already # has one. And, we don't want to alias here because then have to handle # that alias when converting to OpenAI params. So, to support schema, # we use a TypedDict. - schema: Optional[Dict[str, Any]] = None + schema: dict[str, Any] | None = None @json_schema_type @@ -602,11 +589,7 @@ class OpenAIResponseFormatJSONObject(BaseModel): OpenAIResponseFormatParam = Annotated[ - Union[ - OpenAIResponseFormatText, - OpenAIResponseFormatJSONSchema, - OpenAIResponseFormatJSONObject, - ], + OpenAIResponseFormatText | OpenAIResponseFormatJSONSchema | OpenAIResponseFormatJSONObject, Field(discriminator="type"), ] register_schema(OpenAIResponseFormatParam, name="OpenAIResponseFormatParam") @@ -622,7 +605,7 @@ class OpenAITopLogProb(BaseModel): """ token: str - bytes: Optional[List[int]] = None + bytes: list[int] | None = None logprob: float @@ -637,9 +620,9 @@ class OpenAITokenLogProb(BaseModel): """ token: str - bytes: Optional[List[int]] = None + bytes: list[int] | None = None logprob: float - top_logprobs: List[OpenAITopLogProb] + top_logprobs: list[OpenAITopLogProb] @json_schema_type @@ -650,8 +633,8 @@ class OpenAIChoiceLogprobs(BaseModel): :param refusal: (Optional) The log probabilities for the tokens in the message """ - content: Optional[List[OpenAITokenLogProb]] = None - refusal: Optional[List[OpenAITokenLogProb]] = None + content: list[OpenAITokenLogProb] | None = None + refusal: list[OpenAITokenLogProb] | None = None @json_schema_type @@ -664,10 +647,10 @@ class OpenAIChoiceDelta(BaseModel): :param tool_calls: (Optional) The tool calls of the delta """ - content: Optional[str] = None - refusal: Optional[str] = None - role: Optional[str] = None - tool_calls: Optional[List[OpenAIChatCompletionToolCall]] = None + content: str | None = None + refusal: str | None = None + role: str | None = None + tool_calls: list[OpenAIChatCompletionToolCall] | None = None @json_schema_type @@ -683,7 +666,7 @@ class OpenAIChunkChoice(BaseModel): delta: OpenAIChoiceDelta finish_reason: str index: int - logprobs: Optional[OpenAIChoiceLogprobs] = None + logprobs: OpenAIChoiceLogprobs | None = None @json_schema_type @@ -699,7 +682,7 @@ class OpenAIChoice(BaseModel): message: OpenAIMessageParam finish_reason: str index: int - logprobs: Optional[OpenAIChoiceLogprobs] = None + logprobs: OpenAIChoiceLogprobs | None = None @json_schema_type @@ -714,7 +697,7 @@ class OpenAIChatCompletion(BaseModel): """ id: str - choices: List[OpenAIChoice] + choices: list[OpenAIChoice] object: Literal["chat.completion"] = "chat.completion" created: int model: str @@ -732,7 +715,7 @@ class OpenAIChatCompletionChunk(BaseModel): """ id: str - choices: List[OpenAIChunkChoice] + choices: list[OpenAIChunkChoice] object: Literal["chat.completion.chunk"] = "chat.completion.chunk" created: int model: str @@ -748,10 +731,10 @@ class OpenAICompletionLogprobs(BaseModel): :top_logprobs: (Optional) The top log probabilities for the tokens """ - text_offset: Optional[List[int]] = None - token_logprobs: Optional[List[float]] = None - tokens: Optional[List[str]] = None - top_logprobs: Optional[List[Dict[str, float]]] = None + text_offset: list[int] | None = None + token_logprobs: list[float] | None = None + tokens: list[str] | None = None + top_logprobs: list[dict[str, float]] | None = None @json_schema_type @@ -767,7 +750,7 @@ class OpenAICompletionChoice(BaseModel): finish_reason: str text: str index: int - logprobs: Optional[OpenAIChoiceLogprobs] = None + logprobs: OpenAIChoiceLogprobs | None = None @json_schema_type @@ -782,7 +765,7 @@ class OpenAICompletion(BaseModel): """ id: str - choices: List[OpenAICompletionChoice] + choices: list[OpenAICompletionChoice] created: int model: str object: Literal["text_completion"] = "text_completion" @@ -818,12 +801,12 @@ class EmbeddingTaskType(Enum): @json_schema_type class BatchCompletionResponse(BaseModel): - batch: List[CompletionResponse] + batch: list[CompletionResponse] @json_schema_type class BatchChatCompletionResponse(BaseModel): - batch: List[ChatCompletionResponse] + batch: list[ChatCompletionResponse] @runtime_checkable @@ -843,11 +826,11 @@ class Inference(Protocol): self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - ) -> Union[CompletionResponse, AsyncIterator[CompletionResponseStreamChunk]]: + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]: """Generate a completion for the given content using the specified model. :param model_id: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint. @@ -865,10 +848,10 @@ class Inference(Protocol): async def batch_completion( self, model_id: str, - content_batch: List[InterleavedContent], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + content_batch: list[InterleavedContent], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ) -> BatchCompletionResponse: raise NotImplementedError("Batch completion is not implemented") @@ -876,16 +859,16 @@ class Inference(Protocol): async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, - ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]: + messages: list[Message], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, + ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]: """Generate a chat completion for the given messages using the specified model. :param model_id: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint. @@ -916,12 +899,12 @@ class Inference(Protocol): async def batch_chat_completion( self, model_id: str, - messages_batch: List[List[Message]], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_config: Optional[ToolConfig] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + messages_batch: list[list[Message]], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_config: ToolConfig | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ) -> BatchChatCompletionResponse: raise NotImplementedError("Batch chat completion is not implemented") @@ -929,10 +912,10 @@ class Inference(Protocol): async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: """Generate embeddings for content pieces using the specified model. @@ -950,25 +933,25 @@ class Inference(Protocol): self, # Standard OpenAI completion parameters model: str, - prompt: Union[str, List[str], List[int], List[List[int]]], - best_of: Optional[int] = None, - echo: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - presence_penalty: Optional[float] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, # vLLM-specific parameters - guided_choice: Optional[List[str]] = None, - prompt_logprobs: Optional[int] = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, ) -> OpenAICompletion: """Generate an OpenAI-compatible completion for the given prompt using the specified model. @@ -996,29 +979,29 @@ class Inference(Protocol): async def openai_chat_completion( self, model: str, - messages: List[OpenAIMessageParam], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: + messages: list[OpenAIMessageParam], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: """Generate an OpenAI-compatible chat completion for the given messages using the specified model. :param model: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint. diff --git a/llama_stack/apis/inspect/inspect.py b/llama_stack/apis/inspect/inspect.py index 863f90e14..fb3167635 100644 --- a/llama_stack/apis/inspect/inspect.py +++ b/llama_stack/apis/inspect/inspect.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List, Protocol, runtime_checkable +from typing import Protocol, runtime_checkable from pydantic import BaseModel @@ -16,7 +16,7 @@ from llama_stack.schema_utils import json_schema_type, webmethod class RouteInfo(BaseModel): route: str method: str - provider_types: List[str] + provider_types: list[str] @json_schema_type @@ -30,7 +30,7 @@ class VersionInfo(BaseModel): class ListRoutesResponse(BaseModel): - data: List[RouteInfo] + data: list[RouteInfo] @runtime_checkable diff --git a/llama_stack/apis/models/models.py b/llama_stack/apis/models/models.py index 97398ce75..5d7b5aac6 100644 --- a/llama_stack/apis/models/models.py +++ b/llama_stack/apis/models/models.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Any, Dict, List, Literal, Optional, Protocol, runtime_checkable +from typing import Any, Literal, Protocol, runtime_checkable from pydantic import BaseModel, ConfigDict, Field @@ -15,7 +15,7 @@ from llama_stack.schema_utils import json_schema_type, webmethod class CommonModelFields(BaseModel): - metadata: Dict[str, Any] = Field( + metadata: dict[str, Any] = Field( default_factory=dict, description="Any additional metadata for this model", ) @@ -46,14 +46,14 @@ class Model(CommonModelFields, Resource): class ModelInput(CommonModelFields): model_id: str - provider_id: Optional[str] = None - provider_model_id: Optional[str] = None - model_type: Optional[ModelType] = ModelType.llm + provider_id: str | None = None + provider_model_id: str | None = None + model_type: ModelType | None = ModelType.llm model_config = ConfigDict(protected_namespaces=()) class ListModelsResponse(BaseModel): - data: List[Model] + data: list[Model] @json_schema_type @@ -73,7 +73,7 @@ class OpenAIModel(BaseModel): class OpenAIListModelsResponse(BaseModel): - data: List[OpenAIModel] + data: list[OpenAIModel] @runtime_checkable @@ -95,10 +95,10 @@ class Models(Protocol): async def register_model( self, model_id: str, - provider_model_id: Optional[str] = None, - provider_id: Optional[str] = None, - metadata: Optional[Dict[str, Any]] = None, - model_type: Optional[ModelType] = None, + provider_model_id: str | None = None, + provider_id: str | None = None, + metadata: dict[str, Any] | None = None, + model_type: ModelType | None = None, ) -> Model: ... @webmethod(route="/models/{model_id:path}", method="DELETE") diff --git a/llama_stack/apis/post_training/post_training.py b/llama_stack/apis/post_training/post_training.py index e5f1bcb65..016f79fce 100644 --- a/llama_stack/apis/post_training/post_training.py +++ b/llama_stack/apis/post_training/post_training.py @@ -6,10 +6,9 @@ from datetime import datetime from enum import Enum -from typing import Any, Dict, List, Literal, Optional, Protocol, Union +from typing import Annotated, Any, Literal, Protocol from pydantic import BaseModel, Field -from typing_extensions import Annotated from llama_stack.apis.common.content_types import URL from llama_stack.apis.common.job_types import JobStatus @@ -36,9 +35,9 @@ class DataConfig(BaseModel): batch_size: int shuffle: bool data_format: DatasetFormat - validation_dataset_id: Optional[str] = None - packed: Optional[bool] = False - train_on_input: Optional[bool] = False + validation_dataset_id: str | None = None + packed: bool | None = False + train_on_input: bool | None = False @json_schema_type @@ -51,10 +50,10 @@ class OptimizerConfig(BaseModel): @json_schema_type class EfficiencyConfig(BaseModel): - enable_activation_checkpointing: Optional[bool] = False - enable_activation_offloading: Optional[bool] = False - memory_efficient_fsdp_wrap: Optional[bool] = False - fsdp_cpu_offload: Optional[bool] = False + enable_activation_checkpointing: bool | None = False + enable_activation_offloading: bool | None = False + memory_efficient_fsdp_wrap: bool | None = False + fsdp_cpu_offload: bool | None = False @json_schema_type @@ -62,23 +61,23 @@ class TrainingConfig(BaseModel): n_epochs: int max_steps_per_epoch: int = 1 gradient_accumulation_steps: int = 1 - max_validation_steps: Optional[int] = 1 - data_config: Optional[DataConfig] = None - optimizer_config: Optional[OptimizerConfig] = None - efficiency_config: Optional[EfficiencyConfig] = None - dtype: Optional[str] = "bf16" + max_validation_steps: int | None = 1 + data_config: DataConfig | None = None + optimizer_config: OptimizerConfig | None = None + efficiency_config: EfficiencyConfig | None = None + dtype: str | None = "bf16" @json_schema_type class LoraFinetuningConfig(BaseModel): type: Literal["LoRA"] = "LoRA" - lora_attn_modules: List[str] + lora_attn_modules: list[str] apply_lora_to_mlp: bool apply_lora_to_output: bool rank: int alpha: int - use_dora: Optional[bool] = False - quantize_base: Optional[bool] = False + use_dora: bool | None = False + quantize_base: bool | None = False @json_schema_type @@ -88,7 +87,7 @@ class QATFinetuningConfig(BaseModel): group_size: int -AlgorithmConfig = Annotated[Union[LoraFinetuningConfig, QATFinetuningConfig], Field(discriminator="type")] +AlgorithmConfig = Annotated[LoraFinetuningConfig | QATFinetuningConfig, Field(discriminator="type")] register_schema(AlgorithmConfig, name="AlgorithmConfig") @@ -97,7 +96,7 @@ class PostTrainingJobLogStream(BaseModel): """Stream of logs from a finetuning job.""" job_uuid: str - log_lines: List[str] + log_lines: list[str] @json_schema_type @@ -131,8 +130,8 @@ class PostTrainingRLHFRequest(BaseModel): training_config: TrainingConfig # TODO: define these - hyperparam_search_config: Dict[str, Any] - logger_config: Dict[str, Any] + hyperparam_search_config: dict[str, Any] + logger_config: dict[str, Any] class PostTrainingJob(BaseModel): @@ -146,17 +145,17 @@ class PostTrainingJobStatusResponse(BaseModel): job_uuid: str status: JobStatus - scheduled_at: Optional[datetime] = None - started_at: Optional[datetime] = None - completed_at: Optional[datetime] = None + scheduled_at: datetime | None = None + started_at: datetime | None = None + completed_at: datetime | None = None - resources_allocated: Optional[Dict[str, Any]] = None + resources_allocated: dict[str, Any] | None = None - checkpoints: List[Checkpoint] = Field(default_factory=list) + checkpoints: list[Checkpoint] = Field(default_factory=list) class ListPostTrainingJobsResponse(BaseModel): - data: List[PostTrainingJob] + data: list[PostTrainingJob] @json_schema_type @@ -164,7 +163,7 @@ class PostTrainingJobArtifactsResponse(BaseModel): """Artifacts of a finetuning job.""" job_uuid: str - checkpoints: List[Checkpoint] = Field(default_factory=list) + checkpoints: list[Checkpoint] = Field(default_factory=list) # TODO(ashwin): metrics, evals @@ -175,14 +174,14 @@ class PostTraining(Protocol): self, job_uuid: str, training_config: TrainingConfig, - hyperparam_search_config: Dict[str, Any], - logger_config: Dict[str, Any], - model: Optional[str] = Field( + hyperparam_search_config: dict[str, Any], + logger_config: dict[str, Any], + model: str | None = Field( default=None, description="Model descriptor for training if not in provider config`", ), - checkpoint_dir: Optional[str] = None, - algorithm_config: Optional[AlgorithmConfig] = None, + checkpoint_dir: str | None = None, + algorithm_config: AlgorithmConfig | None = None, ) -> PostTrainingJob: ... @webmethod(route="/post-training/preference-optimize", method="POST") @@ -192,8 +191,8 @@ class PostTraining(Protocol): finetuned_model: str, algorithm_config: DPOAlignmentConfig, training_config: TrainingConfig, - hyperparam_search_config: Dict[str, Any], - logger_config: Dict[str, Any], + hyperparam_search_config: dict[str, Any], + logger_config: dict[str, Any], ) -> PostTrainingJob: ... @webmethod(route="/post-training/jobs", method="GET") diff --git a/llama_stack/apis/providers/providers.py b/llama_stack/apis/providers/providers.py index ea5f968ec..751c9263b 100644 --- a/llama_stack/apis/providers/providers.py +++ b/llama_stack/apis/providers/providers.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Protocol, runtime_checkable +from typing import Any, Protocol, runtime_checkable from pydantic import BaseModel @@ -17,12 +17,12 @@ class ProviderInfo(BaseModel): api: str provider_id: str provider_type: str - config: Dict[str, Any] + config: dict[str, Any] health: HealthResponse class ListProvidersResponse(BaseModel): - data: List[ProviderInfo] + data: list[ProviderInfo] @runtime_checkable diff --git a/llama_stack/apis/safety/safety.py b/llama_stack/apis/safety/safety.py index fd2f0292c..e139f5ffc 100644 --- a/llama_stack/apis/safety/safety.py +++ b/llama_stack/apis/safety/safety.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Any, Dict, List, Optional, Protocol, runtime_checkable +from typing import Any, Protocol, runtime_checkable from pydantic import BaseModel, Field @@ -27,16 +27,16 @@ class SafetyViolation(BaseModel): violation_level: ViolationLevel # what message should you convey to the user - user_message: Optional[str] = None + user_message: str | None = None # additional metadata (including specific violation codes) more for # debugging, telemetry - metadata: Dict[str, Any] = Field(default_factory=dict) + metadata: dict[str, Any] = Field(default_factory=dict) @json_schema_type class RunShieldResponse(BaseModel): - violation: Optional[SafetyViolation] = None + violation: SafetyViolation | None = None class ShieldStore(Protocol): @@ -52,6 +52,6 @@ class Safety(Protocol): async def run_shield( self, shield_id: str, - messages: List[Message], - params: Dict[str, Any] = None, + messages: list[Message], + params: dict[str, Any] = None, ) -> RunShieldResponse: ... diff --git a/llama_stack/apis/scoring/scoring.py b/llama_stack/apis/scoring/scoring.py index 54a9ac2aa..414f3d5e2 100644 --- a/llama_stack/apis/scoring/scoring.py +++ b/llama_stack/apis/scoring/scoring.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Optional, Protocol, runtime_checkable +from typing import Any, Protocol, runtime_checkable from pydantic import BaseModel @@ -12,7 +12,7 @@ from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams from llama_stack.schema_utils import json_schema_type, webmethod # mapping of metric to value -ScoringResultRow = Dict[str, Any] +ScoringResultRow = dict[str, Any] @json_schema_type @@ -24,15 +24,15 @@ class ScoringResult(BaseModel): :param aggregated_results: Map of metric name to aggregated value """ - score_rows: List[ScoringResultRow] + score_rows: list[ScoringResultRow] # aggregated metrics to value - aggregated_results: Dict[str, Any] + aggregated_results: dict[str, Any] @json_schema_type class ScoreBatchResponse(BaseModel): - dataset_id: Optional[str] = None - results: Dict[str, ScoringResult] + dataset_id: str | None = None + results: dict[str, ScoringResult] @json_schema_type @@ -44,7 +44,7 @@ class ScoreResponse(BaseModel): """ # each key in the dict is a scoring function name - results: Dict[str, ScoringResult] + results: dict[str, ScoringResult] class ScoringFunctionStore(Protocol): @@ -59,15 +59,15 @@ class Scoring(Protocol): async def score_batch( self, dataset_id: str, - scoring_functions: Dict[str, Optional[ScoringFnParams]], + scoring_functions: dict[str, ScoringFnParams | None], save_results_dataset: bool = False, ) -> ScoreBatchResponse: ... @webmethod(route="/scoring/score", method="POST") async def score( self, - input_rows: List[Dict[str, Any]], - scoring_functions: Dict[str, Optional[ScoringFnParams]], + input_rows: list[dict[str, Any]], + scoring_functions: dict[str, ScoringFnParams | None], ) -> ScoreResponse: """Score a list of rows. diff --git a/llama_stack/apis/scoring_functions/scoring_functions.py b/llama_stack/apis/scoring_functions/scoring_functions.py index 4f85947dd..6c7819965 100644 --- a/llama_stack/apis/scoring_functions/scoring_functions.py +++ b/llama_stack/apis/scoring_functions/scoring_functions.py @@ -6,18 +6,14 @@ from enum import Enum from typing import ( + Annotated, Any, - Dict, - List, Literal, - Optional, Protocol, - Union, runtime_checkable, ) from pydantic import BaseModel, Field -from typing_extensions import Annotated from llama_stack.apis.common.type_system import ParamType from llama_stack.apis.resource import Resource, ResourceType @@ -46,12 +42,12 @@ class AggregationFunctionType(Enum): class LLMAsJudgeScoringFnParams(BaseModel): type: Literal[ScoringFnParamsType.llm_as_judge.value] = ScoringFnParamsType.llm_as_judge.value judge_model: str - prompt_template: Optional[str] = None - judge_score_regexes: Optional[List[str]] = Field( + prompt_template: str | None = None + judge_score_regexes: list[str] | None = Field( description="Regexes to extract the answer from generated response", default_factory=list, ) - aggregation_functions: Optional[List[AggregationFunctionType]] = Field( + aggregation_functions: list[AggregationFunctionType] | None = Field( description="Aggregation functions to apply to the scores of each row", default_factory=list, ) @@ -60,11 +56,11 @@ class LLMAsJudgeScoringFnParams(BaseModel): @json_schema_type class RegexParserScoringFnParams(BaseModel): type: Literal[ScoringFnParamsType.regex_parser.value] = ScoringFnParamsType.regex_parser.value - parsing_regexes: Optional[List[str]] = Field( + parsing_regexes: list[str] | None = Field( description="Regex to extract the answer from generated response", default_factory=list, ) - aggregation_functions: Optional[List[AggregationFunctionType]] = Field( + aggregation_functions: list[AggregationFunctionType] | None = Field( description="Aggregation functions to apply to the scores of each row", default_factory=list, ) @@ -73,33 +69,29 @@ class RegexParserScoringFnParams(BaseModel): @json_schema_type class BasicScoringFnParams(BaseModel): type: Literal[ScoringFnParamsType.basic.value] = ScoringFnParamsType.basic.value - aggregation_functions: Optional[List[AggregationFunctionType]] = Field( + aggregation_functions: list[AggregationFunctionType] | None = Field( description="Aggregation functions to apply to the scores of each row", default_factory=list, ) ScoringFnParams = Annotated[ - Union[ - LLMAsJudgeScoringFnParams, - RegexParserScoringFnParams, - BasicScoringFnParams, - ], + LLMAsJudgeScoringFnParams | RegexParserScoringFnParams | BasicScoringFnParams, Field(discriminator="type"), ] register_schema(ScoringFnParams, name="ScoringFnParams") class CommonScoringFnFields(BaseModel): - description: Optional[str] = None - metadata: Dict[str, Any] = Field( + description: str | None = None + metadata: dict[str, Any] = Field( default_factory=dict, description="Any additional metadata for this definition", ) return_type: ParamType = Field( description="The return type of the deterministic function", ) - params: Optional[ScoringFnParams] = Field( + params: ScoringFnParams | None = Field( description="The parameters for the scoring function for benchmark eval, these can be overridden for app eval", default=None, ) @@ -120,12 +112,12 @@ class ScoringFn(CommonScoringFnFields, Resource): class ScoringFnInput(CommonScoringFnFields, BaseModel): scoring_fn_id: str - provider_id: Optional[str] = None - provider_scoring_fn_id: Optional[str] = None + provider_id: str | None = None + provider_scoring_fn_id: str | None = None class ListScoringFunctionsResponse(BaseModel): - data: List[ScoringFn] + data: list[ScoringFn] @runtime_checkable @@ -142,7 +134,7 @@ class ScoringFunctions(Protocol): scoring_fn_id: str, description: str, return_type: ParamType, - provider_scoring_fn_id: Optional[str] = None, - provider_id: Optional[str] = None, - params: Optional[ScoringFnParams] = None, + provider_scoring_fn_id: str | None = None, + provider_id: str | None = None, + params: ScoringFnParams | None = None, ) -> None: ... diff --git a/llama_stack/apis/shields/shields.py b/llama_stack/apis/shields/shields.py index 67f3bd27b..4172fcbd1 100644 --- a/llama_stack/apis/shields/shields.py +++ b/llama_stack/apis/shields/shields.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Literal, Optional, Protocol, runtime_checkable +from typing import Any, Literal, Protocol, runtime_checkable from pydantic import BaseModel @@ -14,7 +14,7 @@ from llama_stack.schema_utils import json_schema_type, webmethod class CommonShieldFields(BaseModel): - params: Optional[Dict[str, Any]] = None + params: dict[str, Any] | None = None @json_schema_type @@ -34,12 +34,12 @@ class Shield(CommonShieldFields, Resource): class ShieldInput(CommonShieldFields): shield_id: str - provider_id: Optional[str] = None - provider_shield_id: Optional[str] = None + provider_id: str | None = None + provider_shield_id: str | None = None class ListShieldsResponse(BaseModel): - data: List[Shield] + data: list[Shield] @runtime_checkable @@ -55,7 +55,7 @@ class Shields(Protocol): async def register_shield( self, shield_id: str, - provider_shield_id: Optional[str] = None, - provider_id: Optional[str] = None, - params: Optional[Dict[str, Any]] = None, + provider_shield_id: str | None = None, + provider_id: str | None = None, + params: dict[str, Any] | None = None, ) -> Shield: ... diff --git a/llama_stack/apis/synthetic_data_generation/synthetic_data_generation.py b/llama_stack/apis/synthetic_data_generation/synthetic_data_generation.py index 7b41192af..91e550da9 100644 --- a/llama_stack/apis/synthetic_data_generation/synthetic_data_generation.py +++ b/llama_stack/apis/synthetic_data_generation/synthetic_data_generation.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Any, Dict, List, Optional, Protocol, Union +from typing import Any, Protocol from pydantic import BaseModel @@ -28,24 +28,24 @@ class FilteringFunction(Enum): class SyntheticDataGenerationRequest(BaseModel): """Request to generate synthetic data. A small batch of prompts and a filtering function""" - dialogs: List[Message] + dialogs: list[Message] filtering_function: FilteringFunction = FilteringFunction.none - model: Optional[str] = None + model: str | None = None @json_schema_type class SyntheticDataGenerationResponse(BaseModel): """Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold.""" - synthetic_data: List[Dict[str, Any]] - statistics: Optional[Dict[str, Any]] = None + synthetic_data: list[dict[str, Any]] + statistics: dict[str, Any] | None = None class SyntheticDataGeneration(Protocol): @webmethod(route="/synthetic-data-generation/generate") def synthetic_data_generate( self, - dialogs: List[Message], + dialogs: list[Message], filtering_function: FilteringFunction = FilteringFunction.none, - model: Optional[str] = None, - ) -> Union[SyntheticDataGenerationResponse]: ... + model: str | None = None, + ) -> SyntheticDataGenerationResponse: ... diff --git a/llama_stack/apis/telemetry/telemetry.py b/llama_stack/apis/telemetry/telemetry.py index d57c311b2..af0469d2b 100644 --- a/llama_stack/apis/telemetry/telemetry.py +++ b/llama_stack/apis/telemetry/telemetry.py @@ -7,18 +7,14 @@ from datetime import datetime from enum import Enum from typing import ( + Annotated, Any, - Dict, - List, Literal, - Optional, Protocol, - Union, runtime_checkable, ) from pydantic import BaseModel, Field -from typing_extensions import Annotated from llama_stack.models.llama.datatypes import Primitive from llama_stack.schema_utils import json_schema_type, register_schema, webmethod @@ -37,11 +33,11 @@ class SpanStatus(Enum): class Span(BaseModel): span_id: str trace_id: str - parent_span_id: Optional[str] = None + parent_span_id: str | None = None name: str start_time: datetime - end_time: Optional[datetime] = None - attributes: Optional[Dict[str, Any]] = Field(default_factory=dict) + end_time: datetime | None = None + attributes: dict[str, Any] | None = Field(default_factory=dict) def set_attribute(self, key: str, value: Any): if self.attributes is None: @@ -54,7 +50,7 @@ class Trace(BaseModel): trace_id: str root_span_id: str start_time: datetime - end_time: Optional[datetime] = None + end_time: datetime | None = None @json_schema_type @@ -78,7 +74,7 @@ class EventCommon(BaseModel): trace_id: str span_id: str timestamp: datetime - attributes: Optional[Dict[str, Primitive]] = Field(default_factory=dict) + attributes: dict[str, Primitive] | None = Field(default_factory=dict) @json_schema_type @@ -92,15 +88,15 @@ class UnstructuredLogEvent(EventCommon): class MetricEvent(EventCommon): type: Literal[EventType.METRIC.value] = EventType.METRIC.value metric: str # this would be an enum - value: Union[int, float] + value: int | float unit: str @json_schema_type class MetricInResponse(BaseModel): metric: str - value: Union[int, float] - unit: Optional[str] = None + value: int | float + unit: str | None = None # This is a short term solution to allow inference API to return metrics @@ -124,7 +120,7 @@ class MetricInResponse(BaseModel): class MetricResponseMixin(BaseModel): - metrics: Optional[List[MetricInResponse]] = None + metrics: list[MetricInResponse] | None = None @json_schema_type @@ -137,7 +133,7 @@ class StructuredLogType(Enum): class SpanStartPayload(BaseModel): type: Literal[StructuredLogType.SPAN_START.value] = StructuredLogType.SPAN_START.value name: str - parent_span_id: Optional[str] = None + parent_span_id: str | None = None @json_schema_type @@ -147,10 +143,7 @@ class SpanEndPayload(BaseModel): StructuredLogPayload = Annotated[ - Union[ - SpanStartPayload, - SpanEndPayload, - ], + SpanStartPayload | SpanEndPayload, Field(discriminator="type"), ] register_schema(StructuredLogPayload, name="StructuredLogPayload") @@ -163,11 +156,7 @@ class StructuredLogEvent(EventCommon): Event = Annotated[ - Union[ - UnstructuredLogEvent, - MetricEvent, - StructuredLogEvent, - ], + UnstructuredLogEvent | MetricEvent | StructuredLogEvent, Field(discriminator="type"), ] register_schema(Event, name="Event") @@ -184,7 +173,7 @@ class EvalTrace(BaseModel): @json_schema_type class SpanWithStatus(Span): - status: Optional[SpanStatus] = None + status: SpanStatus | None = None @json_schema_type @@ -203,15 +192,15 @@ class QueryCondition(BaseModel): class QueryTracesResponse(BaseModel): - data: List[Trace] + data: list[Trace] class QuerySpansResponse(BaseModel): - data: List[Span] + data: list[Span] class QuerySpanTreeResponse(BaseModel): - data: Dict[str, SpanWithStatus] + data: dict[str, SpanWithStatus] @runtime_checkable @@ -222,10 +211,10 @@ class Telemetry(Protocol): @webmethod(route="/telemetry/traces", method="POST") async def query_traces( self, - attribute_filters: Optional[List[QueryCondition]] = None, - limit: Optional[int] = 100, - offset: Optional[int] = 0, - order_by: Optional[List[str]] = None, + attribute_filters: list[QueryCondition] | None = None, + limit: int | None = 100, + offset: int | None = 0, + order_by: list[str] | None = None, ) -> QueryTracesResponse: ... @webmethod(route="/telemetry/traces/{trace_id:path}", method="GET") @@ -238,23 +227,23 @@ class Telemetry(Protocol): async def get_span_tree( self, span_id: str, - attributes_to_return: Optional[List[str]] = None, - max_depth: Optional[int] = None, + attributes_to_return: list[str] | None = None, + max_depth: int | None = None, ) -> QuerySpanTreeResponse: ... @webmethod(route="/telemetry/spans", method="POST") async def query_spans( self, - attribute_filters: List[QueryCondition], - attributes_to_return: List[str], - max_depth: Optional[int] = None, + attribute_filters: list[QueryCondition], + attributes_to_return: list[str], + max_depth: int | None = None, ) -> QuerySpansResponse: ... @webmethod(route="/telemetry/spans/export", method="POST") async def save_spans_to_dataset( self, - attribute_filters: List[QueryCondition], - attributes_to_save: List[str], + attribute_filters: list[QueryCondition], + attributes_to_save: list[str], dataset_id: str, - max_depth: Optional[int] = None, + max_depth: int | None = None, ) -> None: ... diff --git a/llama_stack/apis/tools/rag_tool.py b/llama_stack/apis/tools/rag_tool.py index 73b36e050..fdf199b1a 100644 --- a/llama_stack/apis/tools/rag_tool.py +++ b/llama_stack/apis/tools/rag_tool.py @@ -5,10 +5,10 @@ # the root directory of this source tree. from enum import Enum -from typing import Any, Dict, List, Literal, Optional, Union +from typing import Annotated, Any, Literal from pydantic import BaseModel, Field -from typing_extensions import Annotated, Protocol, runtime_checkable +from typing_extensions import Protocol, runtime_checkable from llama_stack.apis.common.content_types import URL, InterleavedContent from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol @@ -29,13 +29,13 @@ class RAGDocument(BaseModel): document_id: str content: InterleavedContent | URL mime_type: str | None = None - metadata: Dict[str, Any] = Field(default_factory=dict) + metadata: dict[str, Any] = Field(default_factory=dict) @json_schema_type class RAGQueryResult(BaseModel): - content: Optional[InterleavedContent] = None - metadata: Dict[str, Any] = Field(default_factory=dict) + content: InterleavedContent | None = None + metadata: dict[str, Any] = Field(default_factory=dict) @json_schema_type @@ -59,10 +59,7 @@ class LLMRAGQueryGeneratorConfig(BaseModel): RAGQueryGeneratorConfig = Annotated[ - Union[ - DefaultRAGQueryGeneratorConfig, - LLMRAGQueryGeneratorConfig, - ], + DefaultRAGQueryGeneratorConfig | LLMRAGQueryGeneratorConfig, Field(discriminator="type"), ] register_schema(RAGQueryGeneratorConfig, name="RAGQueryGeneratorConfig") @@ -83,7 +80,7 @@ class RAGToolRuntime(Protocol): @webmethod(route="/tool-runtime/rag-tool/insert", method="POST") async def insert( self, - documents: List[RAGDocument], + documents: list[RAGDocument], vector_db_id: str, chunk_size_in_tokens: int = 512, ) -> None: @@ -94,8 +91,8 @@ class RAGToolRuntime(Protocol): async def query( self, content: InterleavedContent, - vector_db_ids: List[str], - query_config: Optional[RAGQueryConfig] = None, + vector_db_ids: list[str], + query_config: RAGQueryConfig | None = None, ) -> RAGQueryResult: """Query the RAG system for context; typically invoked by the agent""" ... diff --git a/llama_stack/apis/tools/tools.py b/llama_stack/apis/tools/tools.py index 4ca72f71d..eda627932 100644 --- a/llama_stack/apis/tools/tools.py +++ b/llama_stack/apis/tools/tools.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Any, Dict, List, Literal, Optional +from typing import Any, Literal from pydantic import BaseModel, Field from typing_extensions import Protocol, runtime_checkable @@ -24,7 +24,7 @@ class ToolParameter(BaseModel): parameter_type: str description: str required: bool = Field(default=True) - default: Optional[Any] = None + default: Any | None = None @json_schema_type @@ -40,39 +40,39 @@ class Tool(Resource): toolgroup_id: str tool_host: ToolHost description: str - parameters: List[ToolParameter] - metadata: Optional[Dict[str, Any]] = None + parameters: list[ToolParameter] + metadata: dict[str, Any] | None = None @json_schema_type class ToolDef(BaseModel): name: str - description: Optional[str] = None - parameters: Optional[List[ToolParameter]] = None - metadata: Optional[Dict[str, Any]] = None + description: str | None = None + parameters: list[ToolParameter] | None = None + metadata: dict[str, Any] | None = None @json_schema_type class ToolGroupInput(BaseModel): toolgroup_id: str provider_id: str - args: Optional[Dict[str, Any]] = None - mcp_endpoint: Optional[URL] = None + args: dict[str, Any] | None = None + mcp_endpoint: URL | None = None @json_schema_type class ToolGroup(Resource): type: Literal[ResourceType.tool_group.value] = ResourceType.tool_group.value - mcp_endpoint: Optional[URL] = None - args: Optional[Dict[str, Any]] = None + mcp_endpoint: URL | None = None + args: dict[str, Any] | None = None @json_schema_type class ToolInvocationResult(BaseModel): - content: Optional[InterleavedContent] = None - error_message: Optional[str] = None - error_code: Optional[int] = None - metadata: Optional[Dict[str, Any]] = None + content: InterleavedContent | None = None + error_message: str | None = None + error_code: int | None = None + metadata: dict[str, Any] | None = None class ToolStore(Protocol): @@ -81,11 +81,11 @@ class ToolStore(Protocol): class ListToolGroupsResponse(BaseModel): - data: List[ToolGroup] + data: list[ToolGroup] class ListToolsResponse(BaseModel): - data: List[Tool] + data: list[Tool] class ListToolDefsResponse(BaseModel): @@ -100,8 +100,8 @@ class ToolGroups(Protocol): self, toolgroup_id: str, provider_id: str, - mcp_endpoint: Optional[URL] = None, - args: Optional[Dict[str, Any]] = None, + mcp_endpoint: URL | None = None, + args: dict[str, Any] | None = None, ) -> None: """Register a tool group""" ... @@ -118,7 +118,7 @@ class ToolGroups(Protocol): ... @webmethod(route="/tools", method="GET") - async def list_tools(self, toolgroup_id: Optional[str] = None) -> ListToolsResponse: + async def list_tools(self, toolgroup_id: str | None = None) -> ListToolsResponse: """List tools with optional tool group""" ... @@ -151,10 +151,10 @@ class ToolRuntime(Protocol): # TODO: This needs to be renamed once OPEN API generator name conflict issue is fixed. @webmethod(route="/tool-runtime/list-tools", method="GET") async def list_runtime_tools( - self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None + self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None ) -> ListToolDefsResponse: ... @webmethod(route="/tool-runtime/invoke", method="POST") - async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> ToolInvocationResult: + async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult: """Run a tool with the given arguments""" ... diff --git a/llama_stack/apis/vector_dbs/vector_dbs.py b/llama_stack/apis/vector_dbs/vector_dbs.py index fe6c33919..6224566cd 100644 --- a/llama_stack/apis/vector_dbs/vector_dbs.py +++ b/llama_stack/apis/vector_dbs/vector_dbs.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List, Literal, Optional, Protocol, runtime_checkable +from typing import Literal, Protocol, runtime_checkable from pydantic import BaseModel @@ -33,11 +33,11 @@ class VectorDBInput(BaseModel): vector_db_id: str embedding_model: str embedding_dimension: int - provider_vector_db_id: Optional[str] = None + provider_vector_db_id: str | None = None class ListVectorDBsResponse(BaseModel): - data: List[VectorDB] + data: list[VectorDB] @runtime_checkable @@ -57,9 +57,9 @@ class VectorDBs(Protocol): self, vector_db_id: str, embedding_model: str, - embedding_dimension: Optional[int] = 384, - provider_id: Optional[str] = None, - provider_vector_db_id: Optional[str] = None, + embedding_dimension: int | None = 384, + provider_id: str | None = None, + provider_vector_db_id: str | None = None, ) -> VectorDB: ... @webmethod(route="/vector-dbs/{vector_db_id:path}", method="DELETE") diff --git a/llama_stack/apis/vector_io/vector_io.py b/llama_stack/apis/vector_io/vector_io.py index ab0a4a20a..bfae0f802 100644 --- a/llama_stack/apis/vector_io/vector_io.py +++ b/llama_stack/apis/vector_io/vector_io.py @@ -8,7 +8,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Optional, Protocol, runtime_checkable +from typing import Any, Protocol, runtime_checkable from pydantic import BaseModel, Field @@ -20,17 +20,17 @@ from llama_stack.schema_utils import json_schema_type, webmethod class Chunk(BaseModel): content: InterleavedContent - metadata: Dict[str, Any] = Field(default_factory=dict) + metadata: dict[str, Any] = Field(default_factory=dict) @json_schema_type class QueryChunksResponse(BaseModel): - chunks: List[Chunk] - scores: List[float] + chunks: list[Chunk] + scores: list[float] class VectorDBStore(Protocol): - def get_vector_db(self, vector_db_id: str) -> Optional[VectorDB]: ... + def get_vector_db(self, vector_db_id: str) -> VectorDB | None: ... @runtime_checkable @@ -44,8 +44,8 @@ class VectorIO(Protocol): async def insert_chunks( self, vector_db_id: str, - chunks: List[Chunk], - ttl_seconds: Optional[int] = None, + chunks: list[Chunk], + ttl_seconds: int | None = None, ) -> None: ... @webmethod(route="/vector-io/query", method="POST") @@ -53,5 +53,5 @@ class VectorIO(Protocol): self, vector_db_id: str, query: InterleavedContent, - params: Optional[Dict[str, Any]] = None, + params: dict[str, Any] | None = None, ) -> QueryChunksResponse: ... diff --git a/llama_stack/cli/download.py b/llama_stack/cli/download.py index 9694bf22d..09c753776 100644 --- a/llama_stack/cli/download.py +++ b/llama_stack/cli/download.py @@ -13,7 +13,6 @@ from dataclasses import dataclass from datetime import datetime, timezone from functools import partial from pathlib import Path -from typing import Dict, List, Optional import httpx from pydantic import BaseModel, ConfigDict @@ -102,7 +101,7 @@ class DownloadTask: output_file: str total_size: int = 0 downloaded_size: int = 0 - task_id: Optional[int] = None + task_id: int | None = None retries: int = 0 max_retries: int = 3 @@ -262,7 +261,7 @@ class ParallelDownloader: self.progress.update(task.task_id, description=f"[red]Failed: {task.output_file}[/red]") raise DownloadError(f"Download failed for {task.output_file}: {str(e)}") from e - def has_disk_space(self, tasks: List[DownloadTask]) -> bool: + def has_disk_space(self, tasks: list[DownloadTask]) -> bool: try: total_remaining_size = sum(task.total_size - task.downloaded_size for task in tasks) dir_path = os.path.dirname(os.path.abspath(tasks[0].output_file)) @@ -282,7 +281,7 @@ class ParallelDownloader: except Exception as e: raise DownloadError(f"Failed to check disk space: {str(e)}") from e - async def download_all(self, tasks: List[DownloadTask]) -> None: + async def download_all(self, tasks: list[DownloadTask]) -> None: if not tasks: raise ValueError("No download tasks provided") @@ -391,20 +390,20 @@ def _meta_download( class ModelEntry(BaseModel): model_id: str - files: Dict[str, str] + files: dict[str, str] model_config = ConfigDict(protected_namespaces=()) class Manifest(BaseModel): - models: List[ModelEntry] + models: list[ModelEntry] expires_on: datetime def _download_from_manifest(manifest_file: str, max_concurrent_downloads: int): from llama_stack.distribution.utils.model_utils import model_local_dir - with open(manifest_file, "r") as f: + with open(manifest_file) as f: d = json.load(f) manifest = Manifest(**d) @@ -460,15 +459,17 @@ def run_download_cmd(args: argparse.Namespace, parser: argparse.ArgumentParser): from llama_stack.models.llama.sku_list import llama_meta_net_info, resolve_model from .model.safety_models import ( - prompt_guard_download_info, - prompt_guard_model_sku, + prompt_guard_download_info_map, + prompt_guard_model_sku_map, ) - prompt_guard = prompt_guard_model_sku() + prompt_guard_model_sku_map = prompt_guard_model_sku_map() + prompt_guard_download_info_map = prompt_guard_download_info_map() + for model_id in model_ids: - if model_id == prompt_guard.model_id: - model = prompt_guard - info = prompt_guard_download_info() + if model_id in prompt_guard_model_sku_map.keys(): + model = prompt_guard_model_sku_map[model_id] + info = prompt_guard_download_info_map[model_id] else: model = resolve_model(model_id) if model is None: diff --git a/llama_stack/cli/model/describe.py b/llama_stack/cli/model/describe.py index 62dde36e8..26b0da686 100644 --- a/llama_stack/cli/model/describe.py +++ b/llama_stack/cli/model/describe.py @@ -36,11 +36,11 @@ class ModelDescribe(Subcommand): ) def _run_model_describe_cmd(self, args: argparse.Namespace) -> None: - from .safety_models import prompt_guard_model_sku + from .safety_models import prompt_guard_model_sku_map - prompt_guard = prompt_guard_model_sku() - if args.model_id == prompt_guard.model_id: - model = prompt_guard + prompt_guard_model_map = prompt_guard_model_sku_map() + if args.model_id in prompt_guard_model_map.keys(): + model = prompt_guard_model_map[args.model_id] else: model = resolve_model(args.model_id) diff --git a/llama_stack/cli/model/list.py b/llama_stack/cli/model/list.py index b9499f06d..cf84dd526 100644 --- a/llama_stack/cli/model/list.py +++ b/llama_stack/cli/model/list.py @@ -84,7 +84,7 @@ class ModelList(Subcommand): ) def _run_model_list_cmd(self, args: argparse.Namespace) -> None: - from .safety_models import prompt_guard_model_sku + from .safety_models import prompt_guard_model_skus if args.downloaded: return _run_model_list_downloaded_cmd() @@ -96,7 +96,7 @@ class ModelList(Subcommand): ] rows = [] - for model in all_registered_models() + [prompt_guard_model_sku()]: + for model in all_registered_models() + prompt_guard_model_skus(): if not args.show_all and not model.is_featured: continue diff --git a/llama_stack/cli/model/remove.py b/llama_stack/cli/model/remove.py index ee8d6299d..98710d82b 100644 --- a/llama_stack/cli/model/remove.py +++ b/llama_stack/cli/model/remove.py @@ -42,11 +42,12 @@ class ModelRemove(Subcommand): ) def _run_model_remove_cmd(self, args: argparse.Namespace) -> None: - from .safety_models import prompt_guard_model_sku + from .safety_models import prompt_guard_model_sku_map - prompt_guard = prompt_guard_model_sku() - if args.model == prompt_guard.model_id: - model = prompt_guard + prompt_guard_model_map = prompt_guard_model_sku_map() + + if args.model in prompt_guard_model_map.keys(): + model = prompt_guard_model_map[args.model] else: model = resolve_model(args.model) diff --git a/llama_stack/cli/model/safety_models.py b/llama_stack/cli/model/safety_models.py index 131d055aa..e31767f13 100644 --- a/llama_stack/cli/model/safety_models.py +++ b/llama_stack/cli/model/safety_models.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel, ConfigDict, Field @@ -15,14 +15,14 @@ from llama_stack.models.llama.sku_types import CheckpointQuantizationFormat class PromptGuardModel(BaseModel): """Make a 'fake' Model-like object for Prompt Guard. Eventually this will be removed.""" - model_id: str = "Prompt-Guard-86M" + model_id: str + huggingface_repo: str description: str = "Prompt Guard. NOTE: this model will not be provided via `llama` CLI soon." is_featured: bool = False - huggingface_repo: str = "meta-llama/Prompt-Guard-86M" - max_seq_length: int = 2048 + max_seq_length: int = 512 is_instruct_model: bool = False quantization_format: CheckpointQuantizationFormat = CheckpointQuantizationFormat.bf16 - arch_args: Dict[str, Any] = Field(default_factory=dict) + arch_args: dict[str, Any] = Field(default_factory=dict) def descriptor(self) -> str: return self.model_id @@ -30,18 +30,35 @@ class PromptGuardModel(BaseModel): model_config = ConfigDict(protected_namespaces=()) -def prompt_guard_model_sku(): - return PromptGuardModel() +def prompt_guard_model_skus(): + return [ + PromptGuardModel(model_id="Prompt-Guard-86M", huggingface_repo="meta-llama/Prompt-Guard-86M"), + PromptGuardModel( + model_id="Llama-Prompt-Guard-2-86M", + huggingface_repo="meta-llama/Llama-Prompt-Guard-2-86M", + ), + PromptGuardModel( + model_id="Llama-Prompt-Guard-2-22M", + huggingface_repo="meta-llama/Llama-Prompt-Guard-2-22M", + ), + ] -def prompt_guard_download_info(): - return LlamaDownloadInfo( - folder="Prompt-Guard", - files=[ - "model.safetensors", - "special_tokens_map.json", - "tokenizer.json", - "tokenizer_config.json", - ], - pth_size=1, - ) +def prompt_guard_model_sku_map() -> dict[str, Any]: + return {model.model_id: model for model in prompt_guard_model_skus()} + + +def prompt_guard_download_info_map() -> dict[str, LlamaDownloadInfo]: + return { + model.model_id: LlamaDownloadInfo( + folder="Prompt-Guard" if model.model_id == "Prompt-Guard-86M" else model.model_id, + files=[ + "model.safetensors", + "special_tokens_map.json", + "tokenizer.json", + "tokenizer_config.json", + ], + pth_size=1, + ) + for model in prompt_guard_model_skus() + } diff --git a/llama_stack/cli/stack/_build.py b/llama_stack/cli/stack/_build.py index ac1933e0e..ae4a39ce2 100644 --- a/llama_stack/cli/stack/_build.py +++ b/llama_stack/cli/stack/_build.py @@ -13,13 +13,12 @@ import sys import textwrap from functools import lru_cache from pathlib import Path -from typing import Dict, Optional import yaml from prompt_toolkit import prompt from prompt_toolkit.completion import WordCompleter from prompt_toolkit.validation import Validator -from termcolor import cprint +from termcolor import colored, cprint from llama_stack.cli.stack.utils import ImageType from llama_stack.cli.table import print_table @@ -46,14 +45,14 @@ from llama_stack.providers.datatypes import Api TEMPLATES_PATH = Path(__file__).parent.parent.parent / "templates" -@lru_cache() -def available_templates_specs() -> Dict[str, BuildConfig]: +@lru_cache +def available_templates_specs() -> dict[str, BuildConfig]: import yaml template_specs = {} for p in TEMPLATES_PATH.rglob("*build.yaml"): template_name = p.parent.name - with open(p, "r") as f: + with open(p) as f: build_config = BuildConfig(**yaml.safe_load(f)) template_specs[template_name] = build_config return template_specs @@ -89,6 +88,43 @@ def run_stack_build_command(args: argparse.Namespace) -> None: color="red", ) sys.exit(1) + elif args.providers: + providers = dict() + for api_provider in args.providers.split(","): + if "=" not in api_provider: + cprint( + "Could not parse `--providers`. Please ensure the list is in the format api1=provider1,api2=provider2", + color="red", + ) + sys.exit(1) + api, provider = api_provider.split("=") + providers_for_api = get_provider_registry().get(Api(api), None) + if providers_for_api is None: + cprint( + f"{api} is not a valid API.", + color="red", + ) + sys.exit(1) + if provider in providers_for_api: + providers.setdefault(api, []).append(provider) + else: + cprint( + f"{provider} is not a valid provider for the {api} API.", + color="red", + ) + sys.exit(1) + distribution_spec = DistributionSpec( + providers=providers, + description=",".join(args.providers), + ) + if not args.image_type: + cprint( + f"Please specify a image-type (container | conda | venv) for {args.template}", + color="red", + ) + sys.exit(1) + + build_config = BuildConfig(image_type=args.image_type, distribution_spec=distribution_spec) elif not args.config and not args.template: name = prompt( "> Enter a name for your Llama Stack (e.g. my-local-stack): ", @@ -99,12 +135,13 @@ def run_stack_build_command(args: argparse.Namespace) -> None: ) image_type = prompt( - f"> Enter the image type you want your Llama Stack to be built as ({' or '.join(e.value for e in ImageType)}): ", + "> Enter the image type you want your Llama Stack to be built as (use to see options): ", + completer=WordCompleter([e.value for e in ImageType]), + complete_while_typing=True, validator=Validator.from_callable( lambda x: x in [e.value for e in ImageType], - error_message=f"Invalid image type, please enter {' or '.join(e.value for e in ImageType)}", + error_message="Invalid image type. Use to see options", ), - default=ImageType.CONDA.value, ) if image_type == ImageType.CONDA.value: @@ -140,7 +177,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None: if not available_providers: continue api_provider = prompt( - "> Enter provider for API {}: ".format(api.value), + f"> Enter provider for API {api.value}: ", completer=WordCompleter(available_providers), complete_while_typing=True, validator=Validator.from_callable( @@ -163,7 +200,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None: build_config = BuildConfig(image_type=image_type, distribution_spec=distribution_spec) else: - with open(args.config, "r") as f: + with open(args.config) as f: try: build_config = BuildConfig(**yaml.safe_load(f)) except Exception as e: @@ -173,16 +210,9 @@ def run_stack_build_command(args: argparse.Namespace) -> None: ) sys.exit(1) - if build_config.image_type == LlamaStackImageType.CONTAINER.value and not args.image_name: - cprint( - "Please specify --image-name when building a container from a config file", - color="red", - ) - sys.exit(1) - if args.print_deps_only: print(f"# Dependencies for {args.template or args.config or image_name}") - normal_deps, special_deps = get_provider_dependencies(build_config.distribution_spec.providers) + normal_deps, special_deps = get_provider_dependencies(build_config) normal_deps += SERVER_DEPENDENCIES print(f"uv pip install {' '.join(normal_deps)}") for special_dep in special_deps: @@ -198,10 +228,14 @@ def run_stack_build_command(args: argparse.Namespace) -> None: ) except (Exception, RuntimeError) as exc: + import traceback + cprint( f"Error building stack: {exc}", color="red", ) + cprint("Stack trace:", color="red") + traceback.print_exc() sys.exit(1) if run_config is None: cprint( @@ -233,9 +267,10 @@ def _generate_run_config( image_name=image_name, apis=apis, providers={}, + external_providers_dir=build_config.external_providers_dir if build_config.external_providers_dir else None, ) # build providers dict - provider_registry = get_provider_registry() + provider_registry = get_provider_registry(build_config) for api in apis: run_config.providers[api] = [] provider_types = build_config.distribution_spec.providers[api] @@ -249,8 +284,22 @@ def _generate_run_config( if p.deprecation_error: raise InvalidProviderError(p.deprecation_error) - config_type = instantiate_class_type(provider_registry[Api(api)][provider_type].config_class) - if hasattr(config_type, "sample_run_config"): + try: + config_type = instantiate_class_type(provider_registry[Api(api)][provider_type].config_class) + except ModuleNotFoundError: + # HACK ALERT: + # This code executes after building is done, the import cannot work since the + # package is either available in the venv or container - not available on the host. + # TODO: use a "is_external" flag in ProviderSpec to check if the provider is + # external + cprint( + f"Failed to import provider {provider_type} for API {api} - assuming it's external, skipping", + color="yellow", + ) + # Set config_type to None to avoid UnboundLocalError + config_type = None + + if config_type is not None and hasattr(config_type, "sample_run_config"): config = config_type.sample_run_config(__distro_dir__=f"~/.llama/distributions/{image_name}") else: config = {} @@ -268,20 +317,25 @@ def _generate_run_config( to_write = json.loads(run_config.model_dump_json()) f.write(yaml.dump(to_write, sort_keys=False)) - # this path is only invoked when no template is provided - cprint( - f"You can now run your stack with `llama stack run {run_config_file}`", - color="green", - ) + # Only print this message for non-container builds since it will be displayed before the + # container is built + # For non-container builds, the run.yaml is generated at the very end of the build process so it + # makes sense to display this message + if build_config.image_type != LlamaStackImageType.CONTAINER.value: + cprint( + f"You can now run your stack with `llama stack run {run_config_file}`", + color="green", + ) return run_config_file def _run_stack_build_command_from_build_config( build_config: BuildConfig, - image_name: Optional[str] = None, - template_name: Optional[str] = None, - config_path: Optional[str] = None, + image_name: str | None = None, + template_name: str | None = None, + config_path: str | None = None, ) -> str: + image_name = image_name or build_config.image_name if build_config.image_type == LlamaStackImageType.CONTAINER.value: if template_name: image_name = f"distribution-{template_name}" @@ -305,6 +359,13 @@ def _run_stack_build_command_from_build_config( build_file_path = build_dir / f"{image_name}-build.yaml" os.makedirs(build_dir, exist_ok=True) + run_config_file = None + # Generate the run.yaml so it can be included in the container image with the proper entrypoint + # Only do this if we're building a container image and we're not using a template + if build_config.image_type == LlamaStackImageType.CONTAINER.value and not template_name and config_path: + cprint("Generating run.yaml file", color="green") + run_config_file = _generate_run_config(build_config, build_dir, image_name) + with open(build_file_path, "w") as f: to_write = json.loads(build_config.model_dump_json()) f.write(yaml.dump(to_write, sort_keys=False)) @@ -313,7 +374,8 @@ def _run_stack_build_command_from_build_config( build_config, build_file_path, image_name, - template_or_config=template_name or config_path, + template_or_config=template_name or config_path or str(build_file_path), + run_config=run_config_file, ) if return_code != 0: raise RuntimeError(f"Failed to build image {image_name}") @@ -326,6 +388,11 @@ def _run_stack_build_command_from_build_config( shutil.copy(path, run_config_file) cprint("Build Successful!", color="green") + cprint("You can find the newly-built template here: " + colored(template_path, "light_blue")) + cprint( + "You can run the new Llama Stack distro via: " + + colored(f"llama stack run {template_path} --image-type {build_config.image_type}", "light_blue") + ) return template_path else: return _generate_run_config(build_config, build_dir, image_name) diff --git a/llama_stack/cli/stack/build.py b/llama_stack/cli/stack/build.py index c511a0682..93e7d9b22 100644 --- a/llama_stack/cli/stack/build.py +++ b/llama_stack/cli/stack/build.py @@ -75,6 +75,12 @@ the build. If not specified, currently active environment will be used if found. default=False, help="Run the stack after building using the same image type, name, and other applicable arguments", ) + self.parser.add_argument( + "--providers", + type=str, + default=None, + help="Build a config for a list of providers and only those providers. This list is formatted like: api1=provider1,api2=provider2. Where there can be multiple providers per API.", + ) def _run_stack_build_command(self, args: argparse.Namespace) -> None: # always keep implementation completely silo-ed away from CLI so CLI diff --git a/llama_stack/cli/stack/run.py b/llama_stack/cli/stack/run.py index d8234bb46..2eee20883 100644 --- a/llama_stack/cli/stack/run.py +++ b/llama_stack/cli/stack/run.py @@ -119,7 +119,7 @@ class StackRun(Subcommand): if not config_file.is_file(): self.parser.error( - f"Config file must be a valid file path, '{config_file}’ is not a file: type={type(config_file)}" + f"Config file must be a valid file path, '{config_file}' is not a file: type={type(config_file)}" ) logger.info(f"Using run configuration: {config_file}") diff --git a/llama_stack/cli/table.py b/llama_stack/cli/table.py index bf59e6103..86c3adff2 100644 --- a/llama_stack/cli/table.py +++ b/llama_stack/cli/table.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Iterable +from collections.abc import Iterable from rich.console import Console from rich.table import Table diff --git a/llama_stack/cli/verify_download.py b/llama_stack/cli/verify_download.py index 1229e8601..3a1af3cbc 100644 --- a/llama_stack/cli/verify_download.py +++ b/llama_stack/cli/verify_download.py @@ -9,7 +9,6 @@ import hashlib from dataclasses import dataclass from functools import partial from pathlib import Path -from typing import Dict, List, Optional from rich.console import Console from rich.progress import Progress, SpinnerColumn, TextColumn @@ -21,7 +20,7 @@ from llama_stack.cli.subcommand import Subcommand class VerificationResult: filename: str expected_hash: str - actual_hash: Optional[str] + actual_hash: str | None exists: bool matches: bool @@ -60,9 +59,9 @@ def calculate_md5(filepath: Path, chunk_size: int = 8192) -> str: return md5_hash.hexdigest() -def load_checksums(checklist_path: Path) -> Dict[str, str]: +def load_checksums(checklist_path: Path) -> dict[str, str]: checksums = {} - with open(checklist_path, "r") as f: + with open(checklist_path) as f: for line in f: if line.strip(): md5sum, filepath = line.strip().split(" ", 1) @@ -72,7 +71,7 @@ def load_checksums(checklist_path: Path) -> Dict[str, str]: return checksums -def verify_files(model_dir: Path, checksums: Dict[str, str], console: Console) -> List[VerificationResult]: +def verify_files(model_dir: Path, checksums: dict[str, str], console: Console) -> list[VerificationResult]: results = [] with Progress( diff --git a/llama_stack/distribution/access_control.py b/llama_stack/distribution/access_control.py index 0651ab6eb..d560ec80f 100644 --- a/llama_stack/distribution/access_control.py +++ b/llama_stack/distribution/access_control.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from llama_stack.distribution.datatypes import AccessAttributes from llama_stack.log import get_logger @@ -14,8 +14,8 @@ logger = get_logger(__name__, category="core") def check_access( obj_identifier: str, - obj_attributes: Optional[AccessAttributes], - user_attributes: Optional[Dict[str, Any]] = None, + obj_attributes: AccessAttributes | None, + user_attributes: dict[str, Any] | None = None, ) -> bool: """Check if the current user has access to the given object, based on access attributes. diff --git a/llama_stack/distribution/build.py b/llama_stack/distribution/build.py index a8ee372da..1d39063f0 100644 --- a/llama_stack/distribution/build.py +++ b/llama_stack/distribution/build.py @@ -7,16 +7,16 @@ import importlib.resources import logging from pathlib import Path -from typing import Dict, List from pydantic import BaseModel from termcolor import cprint -from llama_stack.distribution.datatypes import BuildConfig, Provider +from llama_stack.distribution.datatypes import BuildConfig from llama_stack.distribution.distribution import get_provider_registry from llama_stack.distribution.utils.exec import run_command from llama_stack.distribution.utils.image_types import LlamaStackImageType from llama_stack.providers.datatypes import Api +from llama_stack.templates.template import DistributionTemplate log = logging.getLogger(__name__) @@ -37,19 +37,23 @@ class ApiInput(BaseModel): def get_provider_dependencies( - config_providers: Dict[str, List[Provider]], + config: BuildConfig | DistributionTemplate, ) -> tuple[list[str], list[str]]: """Get normal and special dependencies from provider configuration.""" - all_providers = get_provider_registry() + # Extract providers based on config type + if isinstance(config, DistributionTemplate): + providers = config.providers + elif isinstance(config, BuildConfig): + providers = config.distribution_spec.providers deps = [] - - for api_str, provider_or_providers in config_providers.items(): - providers_for_api = all_providers[Api(api_str)] + registry = get_provider_registry(config) + for api_str, provider_or_providers in providers.items(): + providers_for_api = registry[Api(api_str)] providers = provider_or_providers if isinstance(provider_or_providers, list) else [provider_or_providers] for provider in providers: - # Providers from BuildConfig and RunConfig are subtly different – not great + # Providers from BuildConfig and RunConfig are subtly different - not great provider_type = provider if isinstance(provider, str) else provider.provider_type if provider_type not in providers_for_api: @@ -71,8 +75,8 @@ def get_provider_dependencies( return list(set(normal_deps)), list(set(special_deps)) -def print_pip_install_help(providers: Dict[str, List[Provider]]): - normal_deps, special_deps = get_provider_dependencies(providers) +def print_pip_install_help(config: BuildConfig): + normal_deps, special_deps = get_provider_dependencies(config) cprint( f"Please install needed dependencies using the following commands:\n\nuv pip install {' '.join(normal_deps)}", @@ -88,10 +92,11 @@ def build_image( build_file_path: Path, image_name: str, template_or_config: str, + run_config: str | None = None, ): container_base = build_config.distribution_spec.container_image or "python:3.10-slim" - normal_deps, special_deps = get_provider_dependencies(build_config.distribution_spec.providers) + normal_deps, special_deps = get_provider_dependencies(build_config) normal_deps += SERVER_DEPENDENCIES if build_config.image_type == LlamaStackImageType.CONTAINER.value: @@ -103,6 +108,11 @@ def build_image( container_base, " ".join(normal_deps), ] + + # When building from a config file (not a template), include the run config path in the + # build arguments + if run_config is not None: + args.append(run_config) elif build_config.image_type == LlamaStackImageType.CONDA.value: script = str(importlib.resources.files("llama_stack") / "distribution/build_conda_env.sh") args = [ diff --git a/llama_stack/distribution/build_container.sh b/llama_stack/distribution/build_container.sh index ed83b7bff..ad316d45e 100755 --- a/llama_stack/distribution/build_container.sh +++ b/llama_stack/distribution/build_container.sh @@ -19,12 +19,16 @@ UV_HTTP_TIMEOUT=${UV_HTTP_TIMEOUT:-500} # mounting is not supported by docker buildx, so we use COPY instead USE_COPY_NOT_MOUNT=${USE_COPY_NOT_MOUNT:-} +# Path to the run.yaml file in the container +RUN_CONFIG_PATH=/app/run.yaml + +BUILD_CONTEXT_DIR=$(pwd) + if [ "$#" -lt 4 ]; then # This only works for templates - echo "Usage: $0 []" >&2 + echo "Usage: $0 [] []" >&2 exit 1 fi - set -euo pipefail template_or_config="$1" @@ -35,8 +39,27 @@ container_base="$1" shift pip_dependencies="$1" shift -special_pip_deps="${1:-}" +# Handle optional arguments +run_config="" +special_pip_deps="" + +# Check if there are more arguments +# The logics is becoming cumbersom, we should refactor it if we can do better +if [ $# -gt 0 ]; then + # Check if the argument ends with .yaml + if [[ "$1" == *.yaml ]]; then + run_config="$1" + shift + # If there's another argument after .yaml, it must be special_pip_deps + if [ $# -gt 0 ]; then + special_pip_deps="$1" + fi + else + # If it's not .yaml, it must be special_pip_deps + special_pip_deps="$1" + fi +fi # Define color codes RED='\033[0;31m' @@ -72,9 +95,13 @@ if [[ $container_base == *"registry.access.redhat.com/ubi9"* ]]; then FROM $container_base WORKDIR /app -RUN dnf -y update && dnf install -y iputils net-tools wget \ +# We install the Python 3.11 dev headers and build tools so that any +# C‑extension wheels (e.g. polyleven, faiss‑cpu) can compile successfully. + +RUN dnf -y update && dnf install -y iputils git net-tools wget \ vim-minimal python3.11 python3.11-pip python3.11-wheel \ - python3.11-setuptools && ln -s /bin/pip3.11 /bin/pip && ln -s /bin/python3.11 /bin/python && dnf clean all + python3.11-setuptools python3.11-devel gcc make && \ + ln -s /bin/pip3.11 /bin/pip && ln -s /bin/python3.11 /bin/python && dnf clean all ENV UV_SYSTEM_PYTHON=1 RUN pip install uv @@ -86,7 +113,7 @@ WORKDIR /app RUN apt-get update && apt-get install -y \ iputils-ping net-tools iproute2 dnsutils telnet \ - curl wget telnet \ + curl wget telnet git\ procps psmisc lsof \ traceroute \ bubblewrap \ @@ -115,6 +142,45 @@ EOF done fi +# Function to get Python command +get_python_cmd() { + if is_command_available python; then + echo "python" + elif is_command_available python3; then + echo "python3" + else + echo "Error: Neither python nor python3 is installed. Please install Python to continue." >&2 + exit 1 + fi +} + +if [ -n "$run_config" ]; then + # Copy the run config to the build context since it's an absolute path + cp "$run_config" "$BUILD_CONTEXT_DIR/run.yaml" + add_to_container << EOF +COPY run.yaml $RUN_CONFIG_PATH +EOF + + # Parse the run.yaml configuration to identify external provider directories + # If external providers are specified, copy their directory to the container + # and update the configuration to reference the new container path + python_cmd=$(get_python_cmd) + external_providers_dir=$($python_cmd -c "import yaml; config = yaml.safe_load(open('$run_config')); print(config.get('external_providers_dir') or '')") + if [ -n "$external_providers_dir" ]; then + echo "Copying external providers directory: $external_providers_dir" + add_to_container << EOF +COPY $external_providers_dir /app/providers.d +EOF + # Edit the run.yaml file to change the external_providers_dir to /app/providers.d + if [ "$(uname)" = "Darwin" ]; then + sed -i.bak -e 's|external_providers_dir:.*|external_providers_dir: /app/providers.d|' "$BUILD_CONTEXT_DIR/run.yaml" + rm -f "$BUILD_CONTEXT_DIR/run.yaml.bak" + else + sed -i 's|external_providers_dir:.*|external_providers_dir: /app/providers.d|' "$BUILD_CONTEXT_DIR/run.yaml" + fi + fi +fi + stack_mount="/app/llama-stack-source" client_mount="/app/llama-stack-client-source" @@ -174,15 +240,16 @@ fi RUN pip uninstall -y uv EOF -# if template_or_config ends with .yaml, it is not a template and we should not use the --template flag -if [[ "$template_or_config" != *.yaml ]]; then +# If a run config is provided, we use the --config flag +if [[ -n "$run_config" ]]; then + add_to_container << EOF +ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server", "--config", "$RUN_CONFIG_PATH"] +EOF +# If a template is provided (not a yaml file), we use the --template flag +elif [[ "$template_or_config" != *.yaml ]]; then add_to_container << EOF ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server", "--template", "$template_or_config"] EOF -else - add_to_container << EOF -ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server"] -EOF fi # Add other require item commands genearic to all containers @@ -254,9 +321,10 @@ $CONTAINER_BINARY build \ "${CLI_ARGS[@]}" \ -t "$image_tag" \ -f "$TEMP_DIR/Containerfile" \ - "." + "$BUILD_CONTEXT_DIR" # clean up tmp/configs +rm -f "$BUILD_CONTEXT_DIR/run.yaml" set +x echo "Success!" diff --git a/llama_stack/distribution/client.py b/llama_stack/distribution/client.py index 1925b864f..9fde8a157 100644 --- a/llama_stack/distribution/client.py +++ b/llama_stack/distribution/client.py @@ -8,7 +8,7 @@ import inspect import json from collections.abc import AsyncIterator from enum import Enum -from typing import Any, Type, Union, get_args, get_origin +from typing import Any, Union, get_args, get_origin import httpx from pydantic import BaseModel, parse_obj_as @@ -27,7 +27,7 @@ async def get_client_impl(protocol, config: RemoteProviderConfig, _deps: Any): return impl -def create_api_client_class(protocol) -> Type: +def create_api_client_class(protocol) -> type: if protocol in _CLIENT_CLASSES: return _CLIENT_CLASSES[protocol] diff --git a/llama_stack/distribution/common.sh b/llama_stack/distribution/common.sh index 15220048b..5f764bcca 100755 --- a/llama_stack/distribution/common.sh +++ b/llama_stack/distribution/common.sh @@ -1,3 +1,5 @@ +#!/usr/bin/env bash + # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # diff --git a/llama_stack/distribution/configure.py b/llama_stack/distribution/configure.py index 2a3bf7053..76167258a 100644 --- a/llama_stack/distribution/configure.py +++ b/llama_stack/distribution/configure.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import logging import textwrap -from typing import Any, Dict +from typing import Any from llama_stack.distribution.datatypes import ( LLAMA_STACK_RUN_CONFIG_VERSION, @@ -24,7 +24,7 @@ from llama_stack.providers.datatypes import Api, ProviderSpec logger = logging.getLogger(__name__) -def configure_single_provider(registry: Dict[str, ProviderSpec], provider: Provider) -> Provider: +def configure_single_provider(registry: dict[str, ProviderSpec], provider: Provider) -> Provider: provider_spec = registry[provider.provider_type] config_type = instantiate_class_type(provider_spec.config_class) try: @@ -120,8 +120,8 @@ def configure_api_providers(config: StackRunConfig, build_spec: DistributionSpec def upgrade_from_routing_table( - config_dict: Dict[str, Any], -) -> Dict[str, Any]: + config_dict: dict[str, Any], +) -> dict[str, Any]: def get_providers(entries): return [ Provider( @@ -163,7 +163,7 @@ def upgrade_from_routing_table( return config_dict -def parse_and_maybe_upgrade_config(config_dict: Dict[str, Any]) -> StackRunConfig: +def parse_and_maybe_upgrade_config(config_dict: dict[str, Any]) -> StackRunConfig: version = config_dict.get("version", None) if version == LLAMA_STACK_RUN_CONFIG_VERSION: return StackRunConfig(**config_dict) diff --git a/llama_stack/distribution/datatypes.py b/llama_stack/distribution/datatypes.py index b24b0ec50..c127136c9 100644 --- a/llama_stack/distribution/datatypes.py +++ b/llama_stack/distribution/datatypes.py @@ -4,7 +4,8 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Annotated, Any, Dict, List, Optional, Union +from enum import Enum +from typing import Annotated, Any from pydantic import BaseModel, Field @@ -29,7 +30,7 @@ LLAMA_STACK_BUILD_CONFIG_VERSION = "2" LLAMA_STACK_RUN_CONFIG_VERSION = "2" -RoutingKey = Union[str, List[str]] +RoutingKey = str | list[str] class AccessAttributes(BaseModel): @@ -46,17 +47,17 @@ class AccessAttributes(BaseModel): """ # Standard attribute categories - the minimal set we need now - roles: Optional[List[str]] = Field( + roles: list[str] | None = Field( default=None, description="Role-based attributes (e.g., 'admin', 'data-scientist', 'user')" ) - teams: Optional[List[str]] = Field(default=None, description="Team-based attributes (e.g., 'ml-team', 'nlp-team')") + teams: list[str] | None = Field(default=None, description="Team-based attributes (e.g., 'ml-team', 'nlp-team')") - projects: Optional[List[str]] = Field( + projects: list[str] | None = Field( default=None, description="Project-based access attributes (e.g., 'llama-3', 'customer-insights')" ) - namespaces: Optional[List[str]] = Field( + namespaces: list[str] | None = Field( default=None, description="Namespace-based access control for resource isolation" ) @@ -105,7 +106,7 @@ class ResourceWithACL(Resource): # ^ User must have access to the customer-insights project AND have confidential namespace """ - access_attributes: Optional[AccessAttributes] = None + access_attributes: AccessAttributes | None = None # Use the extended Resource for all routable objects @@ -141,41 +142,21 @@ class ToolGroupWithACL(ToolGroup, ResourceWithACL): pass -RoutableObject = Union[ - Model, - Shield, - VectorDB, - Dataset, - ScoringFn, - Benchmark, - Tool, - ToolGroup, -] - +RoutableObject = Model | Shield | VectorDB | Dataset | ScoringFn | Benchmark | Tool | ToolGroup RoutableObjectWithProvider = Annotated[ - Union[ - ModelWithACL, - ShieldWithACL, - VectorDBWithACL, - DatasetWithACL, - ScoringFnWithACL, - BenchmarkWithACL, - ToolWithACL, - ToolGroupWithACL, - ], + ModelWithACL + | ShieldWithACL + | VectorDBWithACL + | DatasetWithACL + | ScoringFnWithACL + | BenchmarkWithACL + | ToolWithACL + | ToolGroupWithACL, Field(discriminator="type"), ] -RoutedProtocol = Union[ - Inference, - Safety, - VectorIO, - DatasetIO, - Scoring, - Eval, - ToolRuntime, -] +RoutedProtocol = Inference | Safety | VectorIO | DatasetIO | Scoring | Eval | ToolRuntime # Example: /inference, /safety @@ -183,15 +164,15 @@ class AutoRoutedProviderSpec(ProviderSpec): provider_type: str = "router" config_class: str = "" - container_image: Optional[str] = None + container_image: str | None = None routing_table_api: Api module: str - provider_data_validator: Optional[str] = Field( + provider_data_validator: str | None = Field( default=None, ) @property - def pip_packages(self) -> List[str]: + def pip_packages(self) -> list[str]: raise AssertionError("Should not be called on AutoRoutedProviderSpec") @@ -199,20 +180,20 @@ class AutoRoutedProviderSpec(ProviderSpec): class RoutingTableProviderSpec(ProviderSpec): provider_type: str = "routing_table" config_class: str = "" - container_image: Optional[str] = None + container_image: str | None = None router_api: Api module: str - pip_packages: List[str] = Field(default_factory=list) + pip_packages: list[str] = Field(default_factory=list) class DistributionSpec(BaseModel): - description: Optional[str] = Field( + description: str | None = Field( default="", description="Description of the distribution", ) - container_image: Optional[str] = None - providers: Dict[str, Union[str, List[str]]] = Field( + container_image: str | None = None + providers: dict[str, str | list[str]] = Field( default_factory=dict, description=""" Provider Types for each of the APIs provided by this distribution. If you @@ -224,21 +205,32 @@ in the runtime configuration to help route to the correct provider.""", class Provider(BaseModel): provider_id: str provider_type: str - config: Dict[str, Any] + config: dict[str, Any] class LoggingConfig(BaseModel): - category_levels: Dict[str, str] = Field( - default_factory=Dict, + category_levels: dict[str, str] = Field( + default_factory=dict, description=""" Dictionary of different logging configurations for different portions (ex: core, server) of llama stack""", ) +class AuthProviderType(str, Enum): + """Supported authentication provider types.""" + + KUBERNETES = "kubernetes" + CUSTOM = "custom" + + class AuthenticationConfig(BaseModel): - endpoint: str = Field( + provider_type: AuthProviderType = Field( ..., - description="Endpoint URL to validate authentication tokens", + description="Type of authentication provider (e.g., 'kubernetes', 'custom')", + ) + config: dict[str, str] = Field( + ..., + description="Provider-specific configuration", ) @@ -249,15 +241,15 @@ class ServerConfig(BaseModel): ge=1024, le=65535, ) - tls_certfile: Optional[str] = Field( + tls_certfile: str | None = Field( default=None, description="Path to TLS certificate file for HTTPS", ) - tls_keyfile: Optional[str] = Field( + tls_keyfile: str | None = Field( default=None, description="Path to TLS key file for HTTPS", ) - auth: Optional[AuthenticationConfig] = Field( + auth: AuthenticationConfig | None = Field( default=None, description="Authentication configuration for the server", ) @@ -273,23 +265,23 @@ Reference to the distribution this package refers to. For unregistered (adhoc) p this could be just a hash """, ) - container_image: Optional[str] = Field( + container_image: str | None = Field( default=None, description="Reference to the container image if this package refers to a container", ) - apis: List[str] = Field( + apis: list[str] = Field( default_factory=list, description=""" The list of APIs to serve. If not specified, all APIs specified in the provider_map will be served""", ) - providers: Dict[str, List[Provider]] = Field( + providers: dict[str, list[Provider]] = Field( description=""" One or more providers to use for each API. The same provider_type (e.g., meta-reference) can be instantiated multiple times (with different configs) if necessary. """, ) - metadata_store: Optional[KVStoreConfig] = Field( + metadata_store: KVStoreConfig | None = Field( default=None, description=""" Configuration for the persistence store used by the distribution registry. If not specified, @@ -297,22 +289,22 @@ a default SQLite store will be used.""", ) # registry of "resources" in the distribution - models: List[ModelInput] = Field(default_factory=list) - shields: List[ShieldInput] = Field(default_factory=list) - vector_dbs: List[VectorDBInput] = Field(default_factory=list) - datasets: List[DatasetInput] = Field(default_factory=list) - scoring_fns: List[ScoringFnInput] = Field(default_factory=list) - benchmarks: List[BenchmarkInput] = Field(default_factory=list) - tool_groups: List[ToolGroupInput] = Field(default_factory=list) + models: list[ModelInput] = Field(default_factory=list) + shields: list[ShieldInput] = Field(default_factory=list) + vector_dbs: list[VectorDBInput] = Field(default_factory=list) + datasets: list[DatasetInput] = Field(default_factory=list) + scoring_fns: list[ScoringFnInput] = Field(default_factory=list) + benchmarks: list[BenchmarkInput] = Field(default_factory=list) + tool_groups: list[ToolGroupInput] = Field(default_factory=list) - logging: Optional[LoggingConfig] = Field(default=None, description="Configuration for Llama Stack Logging") + logging: LoggingConfig | None = Field(default=None, description="Configuration for Llama Stack Logging") server: ServerConfig = Field( default_factory=ServerConfig, description="Configuration for the HTTP(S) server", ) - external_providers_dir: Optional[str] = Field( + external_providers_dir: str | None = Field( default=None, description="Path to directory containing external provider implementations. The providers code and dependencies must be installed on the system.", ) @@ -326,3 +318,12 @@ class BuildConfig(BaseModel): default="conda", description="Type of package to build (conda | container | venv)", ) + image_name: str | None = Field( + default=None, + description="Name of the distribution to build", + ) + external_providers_dir: str | None = Field( + default=None, + description="Path to directory containing external provider implementations. The providers packages will be resolved from this directory. " + "pip_packages MUST contain the provider package name.", + ) diff --git a/llama_stack/distribution/distribution.py b/llama_stack/distribution/distribution.py index d4447139c..07a91478a 100644 --- a/llama_stack/distribution/distribution.py +++ b/llama_stack/distribution/distribution.py @@ -7,12 +7,11 @@ import glob import importlib import os -from typing import Any, Dict, List +from typing import Any import yaml from pydantic import BaseModel -from llama_stack.distribution.datatypes import StackRunConfig from llama_stack.log import get_logger from llama_stack.providers.datatypes import ( AdapterSpec, @@ -25,7 +24,7 @@ from llama_stack.providers.datatypes import ( logger = get_logger(name=__name__, category="core") -def stack_apis() -> List[Api]: +def stack_apis() -> list[Api]: return list(Api) @@ -34,7 +33,7 @@ class AutoRoutedApiInfo(BaseModel): router_api: Api -def builtin_automatically_routed_apis() -> List[AutoRoutedApiInfo]: +def builtin_automatically_routed_apis() -> list[AutoRoutedApiInfo]: return [ AutoRoutedApiInfo( routing_table_api=Api.models, @@ -67,12 +66,12 @@ def builtin_automatically_routed_apis() -> List[AutoRoutedApiInfo]: ] -def providable_apis() -> List[Api]: +def providable_apis() -> list[Api]: routing_table_apis = {x.routing_table_api for x in builtin_automatically_routed_apis()} return [api for api in Api if api not in routing_table_apis and api != Api.inspect and api != Api.providers] -def _load_remote_provider_spec(spec_data: Dict[str, Any], api: Api) -> ProviderSpec: +def _load_remote_provider_spec(spec_data: dict[str, Any], api: Api) -> ProviderSpec: adapter = AdapterSpec(**spec_data["adapter"]) spec = remote_provider_spec( api=api, @@ -82,7 +81,7 @@ def _load_remote_provider_spec(spec_data: Dict[str, Any], api: Api) -> ProviderS return spec -def _load_inline_provider_spec(spec_data: Dict[str, Any], api: Api, provider_name: str) -> ProviderSpec: +def _load_inline_provider_spec(spec_data: dict[str, Any], api: Api, provider_name: str) -> ProviderSpec: spec = InlineProviderSpec( api=api, provider_type=f"inline::{provider_name}", @@ -97,7 +96,9 @@ def _load_inline_provider_spec(spec_data: Dict[str, Any], api: Api, provider_nam return spec -def get_provider_registry(config: StackRunConfig | None = None) -> Dict[Api, Dict[str, ProviderSpec]]: +def get_provider_registry( + config=None, +) -> dict[Api, dict[str, ProviderSpec]]: """Get the provider registry, optionally including external providers. This function loads both built-in providers and external providers from YAML files. @@ -122,7 +123,7 @@ def get_provider_registry(config: StackRunConfig | None = None) -> Dict[Api, Dic llama-guard.yaml Args: - config: Optional StackRunConfig containing the external providers directory path + config: Optional object containing the external providers directory path Returns: A dictionary mapping APIs to their available providers @@ -132,7 +133,7 @@ def get_provider_registry(config: StackRunConfig | None = None) -> Dict[Api, Dic ValueError: If any provider spec is invalid """ - ret: Dict[Api, Dict[str, ProviderSpec]] = {} + ret: dict[Api, dict[str, ProviderSpec]] = {} for api in providable_apis(): name = api.name.lower() logger.debug(f"Importing module {name}") @@ -142,7 +143,8 @@ def get_provider_registry(config: StackRunConfig | None = None) -> Dict[Api, Dic except ImportError as e: logger.warning(f"Failed to import module {name}: {e}") - if config and config.external_providers_dir: + # Check if config has the external_providers_dir attribute + if config and hasattr(config, "external_providers_dir") and config.external_providers_dir: external_providers_dir = os.path.abspath(config.external_providers_dir) if not os.path.exists(external_providers_dir): raise FileNotFoundError(f"External providers directory not found: {external_providers_dir}") diff --git a/llama_stack/distribution/library_client.py b/llama_stack/distribution/library_client.py index f426bcafe..b2d16d74c 100644 --- a/llama_stack/distribution/library_client.py +++ b/llama_stack/distribution/library_client.py @@ -12,7 +12,7 @@ import os from concurrent.futures import ThreadPoolExecutor from enum import Enum from pathlib import Path -from typing import Any, Optional, TypeVar, Union, get_args, get_origin +from typing import Any, TypeVar, Union, get_args, get_origin import httpx import yaml @@ -119,8 +119,8 @@ class LlamaStackAsLibraryClient(LlamaStackClient): self, config_path_or_template_name: str, skip_logger_removal: bool = False, - custom_provider_registry: Optional[ProviderRegistry] = None, - provider_data: Optional[dict[str, Any]] = None, + custom_provider_registry: ProviderRegistry | None = None, + provider_data: dict[str, Any] | None = None, ): super().__init__() self.async_client = AsyncLlamaStackAsLibraryClient( @@ -181,8 +181,8 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): def __init__( self, config_path_or_template_name: str, - custom_provider_registry: Optional[ProviderRegistry] = None, - provider_data: Optional[dict[str, Any]] = None, + custom_provider_registry: ProviderRegistry | None = None, + provider_data: dict[str, Any] | None = None, ): super().__init__() # when using the library client, we should not log to console since many @@ -371,7 +371,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): ) return await response.parse() - def _convert_body(self, path: str, method: str, body: Optional[dict] = None) -> dict: + def _convert_body(self, path: str, method: str, body: dict | None = None) -> dict: if not body: return {} diff --git a/llama_stack/distribution/providers.py b/llama_stack/distribution/providers.py index 1c00ce264..157fd1e0e 100644 --- a/llama_stack/distribution/providers.py +++ b/llama_stack/distribution/providers.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import asyncio -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -73,14 +73,14 @@ class ProviderImpl(Providers): raise ValueError(f"Provider {provider_id} not found") - async def get_providers_health(self) -> Dict[str, Dict[str, HealthResponse]]: + async def get_providers_health(self) -> dict[str, dict[str, HealthResponse]]: """Get health status for all providers. Returns: Dict[str, Dict[str, HealthResponse]]: A dictionary mapping API names to provider health statuses. Each API maps to a dictionary of provider IDs to their health responses. """ - providers_health: Dict[str, Dict[str, HealthResponse]] = {} + providers_health: dict[str, dict[str, HealthResponse]] = {} timeout = 1.0 async def check_provider_health(impl: Any) -> tuple[str, HealthResponse] | None: diff --git a/llama_stack/distribution/request_headers.py b/llama_stack/distribution/request_headers.py index f9cde2cdf..bc15776ec 100644 --- a/llama_stack/distribution/request_headers.py +++ b/llama_stack/distribution/request_headers.py @@ -7,7 +7,8 @@ import contextvars import json import logging -from typing import Any, ContextManager, Dict, List, Optional +from contextlib import AbstractContextManager +from typing import Any from .utils.dynamic import instantiate_class_type @@ -17,11 +18,11 @@ log = logging.getLogger(__name__) PROVIDER_DATA_VAR = contextvars.ContextVar("provider_data", default=None) -class RequestProviderDataContext(ContextManager): +class RequestProviderDataContext(AbstractContextManager): """Context manager for request provider data""" def __init__( - self, provider_data: Optional[Dict[str, Any]] = None, auth_attributes: Optional[Dict[str, List[str]]] = None + self, provider_data: dict[str, Any] | None = None, auth_attributes: dict[str, list[str]] | None = None ): self.provider_data = provider_data or {} if auth_attributes: @@ -63,7 +64,7 @@ class NeedsRequestProviderData: return None -def parse_request_provider_data(headers: Dict[str, str]) -> Optional[Dict[str, Any]]: +def parse_request_provider_data(headers: dict[str, str]) -> dict[str, Any] | None: """Parse provider data from request headers""" keys = [ "X-LlamaStack-Provider-Data", @@ -86,14 +87,14 @@ def parse_request_provider_data(headers: Dict[str, str]) -> Optional[Dict[str, A def request_provider_data_context( - headers: Dict[str, str], auth_attributes: Optional[Dict[str, List[str]]] = None -) -> ContextManager: + headers: dict[str, str], auth_attributes: dict[str, list[str]] | None = None +) -> AbstractContextManager: """Context manager that sets request provider data from headers and auth attributes for the duration of the context""" provider_data = parse_request_provider_data(headers) return RequestProviderDataContext(provider_data, auth_attributes) -def get_auth_attributes() -> Optional[Dict[str, List[str]]]: +def get_auth_attributes() -> dict[str, list[str]] | None: """Helper to retrieve auth attributes from the provider data context""" provider_data = PROVIDER_DATA_VAR.get() if not provider_data: diff --git a/llama_stack/distribution/resolver.py b/llama_stack/distribution/resolver.py index e9a594eba..37588ea64 100644 --- a/llama_stack/distribution/resolver.py +++ b/llama_stack/distribution/resolver.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import importlib import inspect -from typing import Any, Dict, List, Set, Tuple +from typing import Any from llama_stack.apis.agents import Agents from llama_stack.apis.benchmarks import Benchmarks @@ -58,7 +58,7 @@ class InvalidProviderError(Exception): pass -def api_protocol_map() -> Dict[Api, Any]: +def api_protocol_map() -> dict[Api, Any]: return { Api.providers: ProvidersAPI, Api.agents: Agents, @@ -83,7 +83,7 @@ def api_protocol_map() -> Dict[Api, Any]: } -def additional_protocols_map() -> Dict[Api, Any]: +def additional_protocols_map() -> dict[Api, Any]: return { Api.inference: (ModelsProtocolPrivate, Models, Api.models), Api.tool_groups: (ToolsProtocolPrivate, ToolGroups, Api.tool_groups), @@ -104,14 +104,14 @@ class ProviderWithSpec(Provider): spec: ProviderSpec -ProviderRegistry = Dict[Api, Dict[str, ProviderSpec]] +ProviderRegistry = dict[Api, dict[str, ProviderSpec]] async def resolve_impls( run_config: StackRunConfig, provider_registry: ProviderRegistry, dist_registry: DistributionRegistry, -) -> Dict[Api, Any]: +) -> dict[Api, Any]: """ Resolves provider implementations by: 1. Validating and organizing providers. @@ -136,7 +136,7 @@ async def resolve_impls( return await instantiate_providers(sorted_providers, router_apis, dist_registry) -def specs_for_autorouted_apis(apis_to_serve: List[str] | Set[str]) -> Dict[str, Dict[str, ProviderWithSpec]]: +def specs_for_autorouted_apis(apis_to_serve: list[str] | set[str]) -> dict[str, dict[str, ProviderWithSpec]]: """Generates specifications for automatically routed APIs.""" specs = {} for info in builtin_automatically_routed_apis(): @@ -178,10 +178,10 @@ def specs_for_autorouted_apis(apis_to_serve: List[str] | Set[str]) -> Dict[str, def validate_and_prepare_providers( - run_config: StackRunConfig, provider_registry: ProviderRegistry, routing_table_apis: Set[Api], router_apis: Set[Api] -) -> Dict[str, Dict[str, ProviderWithSpec]]: + run_config: StackRunConfig, provider_registry: ProviderRegistry, routing_table_apis: set[Api], router_apis: set[Api] +) -> dict[str, dict[str, ProviderWithSpec]]: """Validates providers, handles deprecations, and organizes them into a spec dictionary.""" - providers_with_specs: Dict[str, Dict[str, ProviderWithSpec]] = {} + providers_with_specs: dict[str, dict[str, ProviderWithSpec]] = {} for api_str, providers in run_config.providers.items(): api = Api(api_str) @@ -222,10 +222,10 @@ def validate_provider(provider: Provider, api: Api, provider_registry: ProviderR def sort_providers_by_deps( - providers_with_specs: Dict[str, Dict[str, ProviderWithSpec]], run_config: StackRunConfig -) -> List[Tuple[str, ProviderWithSpec]]: + providers_with_specs: dict[str, dict[str, ProviderWithSpec]], run_config: StackRunConfig +) -> list[tuple[str, ProviderWithSpec]]: """Sorts providers based on their dependencies.""" - sorted_providers: List[Tuple[str, ProviderWithSpec]] = topological_sort( + sorted_providers: list[tuple[str, ProviderWithSpec]] = topological_sort( {k: list(v.values()) for k, v in providers_with_specs.items()} ) @@ -236,11 +236,11 @@ def sort_providers_by_deps( async def instantiate_providers( - sorted_providers: List[Tuple[str, ProviderWithSpec]], router_apis: Set[Api], dist_registry: DistributionRegistry -) -> Dict: + sorted_providers: list[tuple[str, ProviderWithSpec]], router_apis: set[Api], dist_registry: DistributionRegistry +) -> dict: """Instantiates providers asynchronously while managing dependencies.""" - impls: Dict[Api, Any] = {} - inner_impls_by_provider_id: Dict[str, Dict[str, Any]] = {f"inner-{x.value}": {} for x in router_apis} + impls: dict[Api, Any] = {} + inner_impls_by_provider_id: dict[str, dict[str, Any]] = {f"inner-{x.value}": {} for x in router_apis} for api_str, provider in sorted_providers: deps = {a: impls[a] for a in provider.spec.api_dependencies} for a in provider.spec.optional_api_dependencies: @@ -263,9 +263,9 @@ async def instantiate_providers( def topological_sort( - providers_with_specs: Dict[str, List[ProviderWithSpec]], -) -> List[Tuple[str, ProviderWithSpec]]: - def dfs(kv, visited: Set[str], stack: List[str]): + providers_with_specs: dict[str, list[ProviderWithSpec]], +) -> list[tuple[str, ProviderWithSpec]]: + def dfs(kv, visited: set[str], stack: list[str]): api_str, providers = kv visited.add(api_str) @@ -280,8 +280,8 @@ def topological_sort( stack.append(api_str) - visited: Set[str] = set() - stack: List[str] = [] + visited: set[str] = set() + stack: list[str] = [] for api_str, providers in providers_with_specs.items(): if api_str not in visited: @@ -298,8 +298,8 @@ def topological_sort( # returns a class implementing the protocol corresponding to the Api async def instantiate_provider( provider: ProviderWithSpec, - deps: Dict[Api, Any], - inner_impls: Dict[str, Any], + deps: dict[Api, Any], + inner_impls: dict[str, Any], dist_registry: DistributionRegistry, ): protocols = api_protocol_map() @@ -391,8 +391,8 @@ def check_protocol_compliance(obj: Any, protocol: Any) -> None: async def resolve_remote_stack_impls( config: RemoteProviderConfig, - apis: List[str], -) -> Dict[Api, Any]: + apis: list[str], +) -> dict[Api, Any]: protocols = api_protocol_map() additional_protocols = additional_protocols_map() diff --git a/llama_stack/distribution/routers/__init__.py b/llama_stack/distribution/routers/__init__.py index d0fca8771..cd2a296f2 100644 --- a/llama_stack/distribution/routers/__init__.py +++ b/llama_stack/distribution/routers/__init__.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.distribution.datatypes import RoutedProtocol from llama_stack.distribution.store import DistributionRegistry @@ -23,7 +23,7 @@ from .routing_tables import ( async def get_routing_table_impl( api: Api, - impls_by_provider_id: Dict[str, RoutedProtocol], + impls_by_provider_id: dict[str, RoutedProtocol], _deps, dist_registry: DistributionRegistry, ) -> Any: @@ -45,7 +45,7 @@ async def get_routing_table_impl( return impl -async def get_auto_router_impl(api: Api, routing_table: RoutingTable, deps: Dict[str, Any]) -> Any: +async def get_auto_router_impl(api: Api, routing_table: RoutingTable, deps: dict[str, Any]) -> Any: from .routers import ( DatasetIORouter, EvalRouter, diff --git a/llama_stack/distribution/routers/routers.py b/llama_stack/distribution/routers/routers.py index 17aecdaf8..737a384d8 100644 --- a/llama_stack/distribution/routers/routers.py +++ b/llama_stack/distribution/routers/routers.py @@ -6,7 +6,12 @@ import asyncio import time -from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union +from collections.abc import AsyncGenerator, AsyncIterator +from typing import Annotated, Any + +from openai.types.chat import ChatCompletionToolChoiceOptionParam as OpenAIChatCompletionToolChoiceOptionParam +from openai.types.chat import ChatCompletionToolParam as OpenAIChatCompletionToolParam +from pydantic import Field, TypeAdapter from llama_stack.apis.common.content_types import ( URL, @@ -95,9 +100,9 @@ class VectorIORouter(VectorIO): self, vector_db_id: str, embedding_model: str, - embedding_dimension: Optional[int] = 384, - provider_id: Optional[str] = None, - provider_vector_db_id: Optional[str] = None, + embedding_dimension: int | None = 384, + provider_id: str | None = None, + provider_vector_db_id: str | None = None, ) -> None: logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}") await self.routing_table.register_vector_db( @@ -111,8 +116,8 @@ class VectorIORouter(VectorIO): async def insert_chunks( self, vector_db_id: str, - chunks: List[Chunk], - ttl_seconds: Optional[int] = None, + chunks: list[Chunk], + ttl_seconds: int | None = None, ) -> None: logger.debug( f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, ttl_seconds={ttl_seconds}, chunk_ids={[chunk.metadata['document_id'] for chunk in chunks[:3]]}{' and more...' if len(chunks) > 3 else ''}", @@ -123,7 +128,7 @@ class VectorIORouter(VectorIO): self, vector_db_id: str, query: InterleavedContent, - params: Optional[Dict[str, Any]] = None, + params: dict[str, Any] | None = None, ) -> QueryChunksResponse: logger.debug(f"VectorIORouter.query_chunks: {vector_db_id}") return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params) @@ -135,7 +140,7 @@ class InferenceRouter(Inference): def __init__( self, routing_table: RoutingTable, - telemetry: Optional[Telemetry] = None, + telemetry: Telemetry | None = None, ) -> None: logger.debug("Initializing InferenceRouter") self.routing_table = routing_table @@ -155,10 +160,10 @@ class InferenceRouter(Inference): async def register_model( self, model_id: str, - provider_model_id: Optional[str] = None, - provider_id: Optional[str] = None, - metadata: Optional[Dict[str, Any]] = None, - model_type: Optional[ModelType] = None, + provider_model_id: str | None = None, + provider_id: str | None = None, + metadata: dict[str, Any] | None = None, + model_type: ModelType | None = None, ) -> None: logger.debug( f"InferenceRouter.register_model: {model_id=} {provider_model_id=} {provider_id=} {metadata=} {model_type=}", @@ -171,7 +176,7 @@ class InferenceRouter(Inference): completion_tokens: int, total_tokens: int, model: Model, - ) -> List[MetricEvent]: + ) -> list[MetricEvent]: """Constructs a list of MetricEvent objects containing token usage metrics. Args: @@ -216,7 +221,7 @@ class InferenceRouter(Inference): completion_tokens: int, total_tokens: int, model: Model, - ) -> List[MetricInResponse]: + ) -> list[MetricInResponse]: metrics = self._construct_metrics(prompt_tokens, completion_tokens, total_tokens, model) if self.telemetry: for metric in metrics: @@ -225,9 +230,9 @@ class InferenceRouter(Inference): async def _count_tokens( self, - messages: List[Message] | InterleavedContent, - tool_prompt_format: Optional[ToolPromptFormat] = None, - ) -> Optional[int]: + messages: list[Message] | InterleavedContent, + tool_prompt_format: ToolPromptFormat | None = None, + ) -> int | None: if isinstance(messages, list): encoded = self.formatter.encode_dialog_prompt(messages, tool_prompt_format) else: @@ -237,16 +242,16 @@ class InferenceRouter(Inference): async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = None, - tool_prompt_format: Optional[ToolPromptFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, - ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]: + messages: list[Message], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = None, + tool_prompt_format: ToolPromptFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, + ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]: logger.debug( f"InferenceRouter.chat_completion: {model_id=}, {stream=}, {messages=}, {tools=}, {tool_config=}, {response_format=}", ) @@ -346,12 +351,12 @@ class InferenceRouter(Inference): async def batch_chat_completion( self, model_id: str, - messages_batch: List[List[Message]], - tools: Optional[List[ToolDefinition]] = None, - tool_config: Optional[ToolConfig] = None, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + messages_batch: list[list[Message]], + tools: list[ToolDefinition] | None = None, + tool_config: ToolConfig | None = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ) -> BatchChatCompletionResponse: logger.debug( f"InferenceRouter.batch_chat_completion: {model_id=}, {len(messages_batch)=}, {sampling_params=}, {response_format=}, {logprobs=}", @@ -371,10 +376,10 @@ class InferenceRouter(Inference): self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -434,10 +439,10 @@ class InferenceRouter(Inference): async def batch_completion( self, model_id: str, - content_batch: List[InterleavedContent], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + content_batch: list[InterleavedContent], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ) -> BatchCompletionResponse: logger.debug( f"InferenceRouter.batch_completion: {model_id=}, {len(content_batch)=}, {sampling_params=}, {response_format=}, {logprobs=}", @@ -448,10 +453,10 @@ class InferenceRouter(Inference): async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: logger.debug(f"InferenceRouter.embeddings: {model_id}") model = await self.routing_table.get_model(model_id) @@ -470,24 +475,24 @@ class InferenceRouter(Inference): async def openai_completion( self, model: str, - prompt: Union[str, List[str], List[int], List[List[int]]], - best_of: Optional[int] = None, - echo: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - presence_penalty: Optional[float] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - guided_choice: Optional[List[str]] = None, - prompt_logprobs: Optional[int] = None, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, ) -> OpenAICompletion: logger.debug( f"InferenceRouter.openai_completion: {model=}, {stream=}, {prompt=}", @@ -526,29 +531,29 @@ class InferenceRouter(Inference): async def openai_chat_completion( self, model: str, - messages: List[OpenAIMessageParam], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: + messages: Annotated[list[OpenAIMessageParam], Field(..., min_length=1)], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: logger.debug( f"InferenceRouter.openai_chat_completion: {model=}, {stream=}, {messages=}", ) @@ -558,6 +563,16 @@ class InferenceRouter(Inference): if model_obj.model_type == ModelType.embedding: raise ValueError(f"Model '{model}' is an embedding model and does not support chat completions") + # Use the OpenAI client for a bit of extra input validation without + # exposing the OpenAI client itself as part of our API surface + if tool_choice: + TypeAdapter(OpenAIChatCompletionToolChoiceOptionParam).validate_python(tool_choice) + if tools is None: + raise ValueError("'tool_choice' is only allowed when 'tools' is also provided") + if tools: + for tool in tools: + TypeAdapter(OpenAIChatCompletionToolParam).validate_python(tool) + params = dict( model=model_obj.identifier, messages=messages, @@ -587,7 +602,7 @@ class InferenceRouter(Inference): provider = self.routing_table.get_provider_impl(model_obj.identifier) return await provider.openai_chat_completion(**params) - async def health(self) -> Dict[str, HealthResponse]: + async def health(self) -> dict[str, HealthResponse]: health_statuses = {} timeout = 0.5 for provider_id, impl in self.routing_table.impls_by_provider_id.items(): @@ -630,9 +645,9 @@ class SafetyRouter(Safety): async def register_shield( self, shield_id: str, - provider_shield_id: Optional[str] = None, - provider_id: Optional[str] = None, - params: Optional[Dict[str, Any]] = None, + provider_shield_id: str | None = None, + provider_id: str | None = None, + params: dict[str, Any] | None = None, ) -> Shield: logger.debug(f"SafetyRouter.register_shield: {shield_id}") return await self.routing_table.register_shield(shield_id, provider_shield_id, provider_id, params) @@ -640,8 +655,8 @@ class SafetyRouter(Safety): async def run_shield( self, shield_id: str, - messages: List[Message], - params: Dict[str, Any] = None, + messages: list[Message], + params: dict[str, Any] = None, ) -> RunShieldResponse: logger.debug(f"SafetyRouter.run_shield: {shield_id}") return await self.routing_table.get_provider_impl(shield_id).run_shield( @@ -671,8 +686,8 @@ class DatasetIORouter(DatasetIO): self, purpose: DatasetPurpose, source: DataSource, - metadata: Optional[Dict[str, Any]] = None, - dataset_id: Optional[str] = None, + metadata: dict[str, Any] | None = None, + dataset_id: str | None = None, ) -> None: logger.debug( f"DatasetIORouter.register_dataset: {purpose=} {source=} {metadata=} {dataset_id=}", @@ -687,8 +702,8 @@ class DatasetIORouter(DatasetIO): async def iterrows( self, dataset_id: str, - start_index: Optional[int] = None, - limit: Optional[int] = None, + start_index: int | None = None, + limit: int | None = None, ) -> PaginatedResponse: logger.debug( f"DatasetIORouter.iterrows: {dataset_id}, {start_index=} {limit=}", @@ -699,7 +714,7 @@ class DatasetIORouter(DatasetIO): limit=limit, ) - async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None: + async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None: logger.debug(f"DatasetIORouter.append_rows: {dataset_id}, {len(rows)} rows") return await self.routing_table.get_provider_impl(dataset_id).append_rows( dataset_id=dataset_id, @@ -726,7 +741,7 @@ class ScoringRouter(Scoring): async def score_batch( self, dataset_id: str, - scoring_functions: Dict[str, Optional[ScoringFnParams]] = None, + scoring_functions: dict[str, ScoringFnParams | None] = None, save_results_dataset: bool = False, ) -> ScoreBatchResponse: logger.debug(f"ScoringRouter.score_batch: {dataset_id}") @@ -747,8 +762,8 @@ class ScoringRouter(Scoring): async def score( self, - input_rows: List[Dict[str, Any]], - scoring_functions: Dict[str, Optional[ScoringFnParams]] = None, + input_rows: list[dict[str, Any]], + scoring_functions: dict[str, ScoringFnParams | None] = None, ) -> ScoreResponse: logger.debug(f"ScoringRouter.score: {len(input_rows)} rows, {len(scoring_functions)} functions") res = {} @@ -793,8 +808,8 @@ class EvalRouter(Eval): async def evaluate_rows( self, benchmark_id: str, - input_rows: List[Dict[str, Any]], - scoring_functions: List[str], + input_rows: list[dict[str, Any]], + scoring_functions: list[str], benchmark_config: BenchmarkConfig, ) -> EvaluateResponse: logger.debug(f"EvalRouter.evaluate_rows: {benchmark_id}, {len(input_rows)} rows") @@ -848,8 +863,8 @@ class ToolRuntimeRouter(ToolRuntime): async def query( self, content: InterleavedContent, - vector_db_ids: List[str], - query_config: Optional[RAGQueryConfig] = None, + vector_db_ids: list[str], + query_config: RAGQueryConfig | None = None, ) -> RAGQueryResult: logger.debug(f"ToolRuntimeRouter.RagToolImpl.query: {vector_db_ids}") return await self.routing_table.get_provider_impl("knowledge_search").query( @@ -858,7 +873,7 @@ class ToolRuntimeRouter(ToolRuntime): async def insert( self, - documents: List[RAGDocument], + documents: list[RAGDocument], vector_db_id: str, chunk_size_in_tokens: int = 512, ) -> None: @@ -889,7 +904,7 @@ class ToolRuntimeRouter(ToolRuntime): logger.debug("ToolRuntimeRouter.shutdown") pass - async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> Any: + async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> Any: logger.debug(f"ToolRuntimeRouter.invoke_tool: {tool_name}") return await self.routing_table.get_provider_impl(tool_name).invoke_tool( tool_name=tool_name, @@ -897,7 +912,7 @@ class ToolRuntimeRouter(ToolRuntime): ) async def list_runtime_tools( - self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None + self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None ) -> ListToolDefsResponse: logger.debug(f"ToolRuntimeRouter.list_runtime_tools: {tool_group_id}") return await self.routing_table.get_provider_impl(tool_group_id).list_tools(tool_group_id, mcp_endpoint) diff --git a/llama_stack/distribution/routers/routing_tables.py b/llama_stack/distribution/routers/routing_tables.py index 18b0c891f..c04562197 100644 --- a/llama_stack/distribution/routers/routing_tables.py +++ b/llama_stack/distribution/routers/routing_tables.py @@ -7,7 +7,7 @@ import logging import time import uuid -from typing import Any, Dict, List, Optional +from typing import Any from pydantic import TypeAdapter @@ -106,20 +106,20 @@ async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None: raise ValueError(f"Unregister not supported for {api}") -Registry = Dict[str, List[RoutableObjectWithProvider]] +Registry = dict[str, list[RoutableObjectWithProvider]] class CommonRoutingTableImpl(RoutingTable): def __init__( self, - impls_by_provider_id: Dict[str, RoutedProtocol], + impls_by_provider_id: dict[str, RoutedProtocol], dist_registry: DistributionRegistry, ) -> None: self.impls_by_provider_id = impls_by_provider_id self.dist_registry = dist_registry async def initialize(self) -> None: - async def add_objects(objs: List[RoutableObjectWithProvider], provider_id: str, cls) -> None: + async def add_objects(objs: list[RoutableObjectWithProvider], provider_id: str, cls) -> None: for obj in objs: if cls is None: obj.provider_id = provider_id @@ -154,7 +154,7 @@ class CommonRoutingTableImpl(RoutingTable): for p in self.impls_by_provider_id.values(): await p.shutdown() - def get_provider_impl(self, routing_key: str, provider_id: Optional[str] = None) -> Any: + def get_provider_impl(self, routing_key: str, provider_id: str | None = None) -> Any: def apiname_object(): if isinstance(self, ModelsRoutingTable): return ("Inference", "model") @@ -192,7 +192,7 @@ class CommonRoutingTableImpl(RoutingTable): raise ValueError(f"Provider not found for `{routing_key}`") - async def get_object_by_identifier(self, type: str, identifier: str) -> Optional[RoutableObjectWithProvider]: + async def get_object_by_identifier(self, type: str, identifier: str) -> RoutableObjectWithProvider | None: # Get from disk registry obj = await self.dist_registry.get(type, identifier) if not obj: @@ -236,7 +236,7 @@ class CommonRoutingTableImpl(RoutingTable): await self.dist_registry.register(obj) return obj - async def get_all_with_type(self, type: str) -> List[RoutableObjectWithProvider]: + async def get_all_with_type(self, type: str) -> list[RoutableObjectWithProvider]: objs = await self.dist_registry.get_all() filtered_objs = [obj for obj in objs if obj.type == type] @@ -277,10 +277,10 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models): async def register_model( self, model_id: str, - provider_model_id: Optional[str] = None, - provider_id: Optional[str] = None, - metadata: Optional[Dict[str, Any]] = None, - model_type: Optional[ModelType] = None, + provider_model_id: str | None = None, + provider_id: str | None = None, + metadata: dict[str, Any] | None = None, + model_type: ModelType | None = None, ) -> Model: if provider_model_id is None: provider_model_id = model_id @@ -328,9 +328,9 @@ class ShieldsRoutingTable(CommonRoutingTableImpl, Shields): async def register_shield( self, shield_id: str, - provider_shield_id: Optional[str] = None, - provider_id: Optional[str] = None, - params: Optional[Dict[str, Any]] = None, + provider_shield_id: str | None = None, + provider_id: str | None = None, + params: dict[str, Any] | None = None, ) -> Shield: if provider_shield_id is None: provider_shield_id = shield_id @@ -368,9 +368,9 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): self, vector_db_id: str, embedding_model: str, - embedding_dimension: Optional[int] = 384, - provider_id: Optional[str] = None, - provider_vector_db_id: Optional[str] = None, + embedding_dimension: int | None = 384, + provider_id: str | None = None, + provider_vector_db_id: str | None = None, ) -> VectorDB: if provider_vector_db_id is None: provider_vector_db_id = vector_db_id @@ -423,8 +423,8 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets): self, purpose: DatasetPurpose, source: DataSource, - metadata: Optional[Dict[str, Any]] = None, - dataset_id: Optional[str] = None, + metadata: dict[str, Any] | None = None, + dataset_id: str | None = None, ) -> Dataset: if isinstance(source, dict): if source["type"] == "uri": @@ -438,7 +438,10 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets): provider_dataset_id = dataset_id # infer provider from source - if source.type == DatasetType.rows.value: + if metadata: + if metadata.get("provider_id"): + provider_id = metadata.get("provider_id") # pass through from nvidia datasetio + elif source.type == DatasetType.rows.value: provider_id = "localfs" elif source.type == DatasetType.uri.value: # infer provider from uri @@ -486,9 +489,9 @@ class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions): scoring_fn_id: str, description: str, return_type: ParamType, - provider_scoring_fn_id: Optional[str] = None, - provider_id: Optional[str] = None, - params: Optional[ScoringFnParams] = None, + provider_scoring_fn_id: str | None = None, + provider_id: str | None = None, + params: ScoringFnParams | None = None, ) -> None: if provider_scoring_fn_id is None: provider_scoring_fn_id = scoring_fn_id @@ -525,10 +528,10 @@ class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks): self, benchmark_id: str, dataset_id: str, - scoring_functions: List[str], - metadata: Optional[Dict[str, Any]] = None, - provider_benchmark_id: Optional[str] = None, - provider_id: Optional[str] = None, + scoring_functions: list[str], + metadata: dict[str, Any] | None = None, + provider_benchmark_id: str | None = None, + provider_id: str | None = None, ) -> None: if metadata is None: metadata = {} @@ -553,7 +556,7 @@ class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks): class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups): - async def list_tools(self, toolgroup_id: Optional[str] = None) -> ListToolsResponse: + async def list_tools(self, toolgroup_id: str | None = None) -> ListToolsResponse: tools = await self.get_all_with_type("tool") if toolgroup_id: tools = [tool for tool in tools if tool.toolgroup_id == toolgroup_id] @@ -575,8 +578,8 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups): self, toolgroup_id: str, provider_id: str, - mcp_endpoint: Optional[URL] = None, - args: Optional[Dict[str, Any]] = None, + mcp_endpoint: URL | None = None, + args: dict[str, Any] | None = None, ) -> None: tools = [] tool_defs = await self.impls_by_provider_id[provider_id].list_runtime_tools(toolgroup_id, mcp_endpoint) diff --git a/llama_stack/distribution/server/auth.py b/llama_stack/distribution/server/auth.py index 52e6a013c..429232ece 100644 --- a/llama_stack/distribution/server/auth.py +++ b/llama_stack/distribution/server/auth.py @@ -5,74 +5,29 @@ # the root directory of this source tree. import json -from typing import Dict, List, Optional -from urllib.parse import parse_qs import httpx -from pydantic import BaseModel, Field -from llama_stack.distribution.datatypes import AccessAttributes +from llama_stack.distribution.server.auth_providers import AuthProviderConfig, create_auth_provider from llama_stack.log import get_logger logger = get_logger(name=__name__, category="auth") -class AuthRequestContext(BaseModel): - path: str = Field(description="The path of the request being authenticated") - - headers: Dict[str, str] = Field(description="HTTP headers from the original request (excluding Authorization)") - - params: Dict[str, List[str]] = Field( - description="Query parameters from the original request, parsed as dictionary of lists" - ) - - -class AuthRequest(BaseModel): - api_key: str = Field(description="The API key extracted from the Authorization header") - - request: AuthRequestContext = Field(description="Context information about the request being authenticated") - - -class AuthResponse(BaseModel): - """The format of the authentication response from the auth endpoint.""" - - access_attributes: Optional[AccessAttributes] = Field( - default=None, - description=""" - Structured user attributes for attribute-based access control. - - These attributes determine which resources the user can access. - The model provides standard categories like "roles", "teams", "projects", and "namespaces". - Each attribute category contains a list of values that the user has for that category. - During access control checks, these values are compared against resource requirements. - - Example with standard categories: - ```json - { - "roles": ["admin", "data-scientist"], - "teams": ["ml-team"], - "projects": ["llama-3"], - "namespaces": ["research"] - } - ``` - """, - ) - - message: Optional[str] = Field( - default=None, description="Optional message providing additional context about the authentication result." - ) - - class AuthenticationMiddleware: - """Middleware that authenticates requests using an external auth endpoint. + """Middleware that authenticates requests using configured authentication provider. This middleware: 1. Extracts the Bearer token from the Authorization header - 2. Sends it to the configured auth endpoint along with request details - 3. Validates the response and extracts user attributes + 2. Uses the configured auth provider to validate the token + 3. Extracts user attributes from the provider's response 4. Makes these attributes available to the route handlers for access control - Authentication Request Format: + The middleware supports multiple authentication providers through the AuthProvider interface: + - Kubernetes: Validates tokens against the Kubernetes API server + - Custom: Validates tokens against a custom endpoint + + Authentication Request Format for Custom Auth Provider: ```json { "api_key": "the-api-key-extracted-from-auth-header", @@ -105,21 +60,26 @@ class AuthenticationMiddleware: } ``` + Token Validation: + Each provider implements its own token validation logic: + - Kubernetes: Uses TokenReview API to validate service account tokens + - Custom: Sends token to custom endpoint for validation + Attribute-Based Access Control: - The attributes returned by the auth endpoint are used to determine which + The attributes returned by the auth provider are used to determine which resources the user can access. Resources can specify required attributes using the access_attributes field. For a user to access a resource: 1. All attribute categories specified in the resource must be present in the user's attributes 2. For each category, the user must have at least one matching value - If the auth endpoint doesn't return any attributes, the user will only be able to + If the auth provider doesn't return any attributes, the user will only be able to access resources that don't have access_attributes defined. """ - def __init__(self, app, auth_endpoint): + def __init__(self, app, auth_config: AuthProviderConfig): self.app = app - self.auth_endpoint = auth_endpoint + self.auth_provider = create_auth_provider(auth_config) async def __call__(self, scope, receive, send): if scope["type"] == "http": @@ -129,66 +89,34 @@ class AuthenticationMiddleware: if not auth_header or not auth_header.startswith("Bearer "): return await self._send_auth_error(send, "Missing or invalid Authorization header") - api_key = auth_header.split("Bearer ", 1)[1] + token = auth_header.split("Bearer ", 1)[1] - path = scope.get("path", "") - request_headers = {k.decode(): v.decode() for k, v in headers.items()} - - # Remove sensitive headers - if "authorization" in request_headers: - del request_headers["authorization"] - - query_string = scope.get("query_string", b"").decode() - params = parse_qs(query_string) - - # Build the auth request model - auth_request = AuthRequest( - api_key=api_key, - request=AuthRequestContext( - path=path, - headers=request_headers, - params=params, - ), - ) - - # Validate with authentication endpoint + # Validate token and get access attributes try: - async with httpx.AsyncClient() as client: - response = await client.post( - self.auth_endpoint, - json=auth_request.model_dump(), - timeout=10.0, # Add a reasonable timeout - ) - if response.status_code != 200: - logger.warning(f"Authentication failed: {response.status_code}") - return await self._send_auth_error(send, "Authentication failed") - - # Parse and validate the auth response - try: - response_data = response.json() - auth_response = AuthResponse(**response_data) - - # Store attributes in request scope for access control - if auth_response.access_attributes: - user_attributes = auth_response.access_attributes.model_dump(exclude_none=True) - else: - logger.warning("No access attributes, setting namespace to api_key by default") - user_attributes = { - "namespaces": [api_key], - } - - scope["user_attributes"] = user_attributes - logger.debug(f"Authentication successful: {len(user_attributes)} attributes") - except Exception: - logger.exception("Error parsing authentication response") - return await self._send_auth_error(send, "Invalid authentication response format") + access_attributes = await self.auth_provider.validate_token(token, scope) except httpx.TimeoutException: logger.exception("Authentication request timed out") return await self._send_auth_error(send, "Authentication service timeout") + except ValueError as e: + logger.exception("Error during authentication") + return await self._send_auth_error(send, str(e)) except Exception: logger.exception("Error during authentication") return await self._send_auth_error(send, "Authentication service error") + # Store attributes in request scope for access control + if access_attributes: + user_attributes = access_attributes.model_dump(exclude_none=True) + else: + logger.warning("No access attributes, setting namespace to token by default") + user_attributes = { + "namespaces": [token], + } + + # Store attributes in request scope + scope["user_attributes"] = user_attributes + logger.debug(f"Authentication successful: {len(scope['user_attributes'])} attributes") + return await self.app(scope, receive, send) async def _send_auth_error(self, send, message): diff --git a/llama_stack/distribution/server/auth_providers.py b/llama_stack/distribution/server/auth_providers.py new file mode 100644 index 000000000..1b19f8923 --- /dev/null +++ b/llama_stack/distribution/server/auth_providers.py @@ -0,0 +1,261 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import json +from abc import ABC, abstractmethod +from enum import Enum +from urllib.parse import parse_qs + +import httpx +from pydantic import BaseModel, Field + +from llama_stack.distribution.datatypes import AccessAttributes +from llama_stack.log import get_logger + +logger = get_logger(name=__name__, category="auth") + + +class AuthResponse(BaseModel): + """The format of the authentication response from the auth endpoint.""" + + access_attributes: AccessAttributes | None = Field( + default=None, + description=""" + Structured user attributes for attribute-based access control. + + These attributes determine which resources the user can access. + The model provides standard categories like "roles", "teams", "projects", and "namespaces". + Each attribute category contains a list of values that the user has for that category. + During access control checks, these values are compared against resource requirements. + + Example with standard categories: + ```json + { + "roles": ["admin", "data-scientist"], + "teams": ["ml-team"], + "projects": ["llama-3"], + "namespaces": ["research"] + } + ``` + """, + ) + + message: str | None = Field( + default=None, description="Optional message providing additional context about the authentication result." + ) + + +class AuthRequestContext(BaseModel): + path: str = Field(description="The path of the request being authenticated") + + headers: dict[str, str] = Field(description="HTTP headers from the original request (excluding Authorization)") + + params: dict[str, list[str]] = Field( + description="Query parameters from the original request, parsed as dictionary of lists" + ) + + +class AuthRequest(BaseModel): + api_key: str = Field(description="The API key extracted from the Authorization header") + + request: AuthRequestContext = Field(description="Context information about the request being authenticated") + + +class AuthProviderType(str, Enum): + """Supported authentication provider types.""" + + KUBERNETES = "kubernetes" + CUSTOM = "custom" + + +class AuthProviderConfig(BaseModel): + """Base configuration for authentication providers.""" + + provider_type: AuthProviderType = Field(..., description="Type of authentication provider") + config: dict[str, str] = Field(..., description="Provider-specific configuration") + + +class AuthProvider(ABC): + """Abstract base class for authentication providers.""" + + @abstractmethod + async def validate_token(self, token: str, scope: dict | None = None) -> AccessAttributes | None: + """Validate a token and return access attributes.""" + pass + + @abstractmethod + async def close(self): + """Clean up any resources.""" + pass + + +class KubernetesAuthProvider(AuthProvider): + """Kubernetes authentication provider that validates tokens against the Kubernetes API server.""" + + def __init__(self, config: dict[str, str]): + self.api_server_url = config["api_server_url"] + self.ca_cert_path = config.get("ca_cert_path") + self._client = None + + async def _get_client(self): + """Get or create a Kubernetes client.""" + if self._client is None: + # kubernetes-client has not async support, see: + # https://github.com/kubernetes-client/python/issues/323 + from kubernetes import client + from kubernetes.client import ApiClient + + # Configure the client + configuration = client.Configuration() + configuration.host = self.api_server_url + if self.ca_cert_path: + configuration.ssl_ca_cert = self.ca_cert_path + configuration.verify_ssl = bool(self.ca_cert_path) + + # Create API client + self._client = ApiClient(configuration) + return self._client + + async def validate_token(self, token: str, scope: dict | None = None) -> AccessAttributes | None: + """Validate a Kubernetes token and return access attributes.""" + try: + client = await self._get_client() + + # Set the token in the client + client.set_default_header("Authorization", f"Bearer {token}") + + # Make a request to validate the token + # We use the /api endpoint which requires authentication + from kubernetes.client import CoreV1Api + + api = CoreV1Api(client) + api.get_api_resources(_request_timeout=3.0) # Set timeout for this specific request + + # If we get here, the token is valid + # Extract user info from the token claims + import base64 + + # Decode the token (without verification since we've already validated it) + token_parts = token.split(".") + payload = json.loads(base64.b64decode(token_parts[1] + "=" * (-len(token_parts[1]) % 4))) + + # Extract user information from the token + username = payload.get("sub", "") + groups = payload.get("groups", []) + + return AccessAttributes( + roles=[username], # Use username as a role + teams=groups, # Use Kubernetes groups as teams + ) + + except Exception as e: + logger.exception("Failed to validate Kubernetes token") + raise ValueError("Invalid or expired token") from e + + async def close(self): + """Close the HTTP client.""" + if self._client: + self._client.close() + self._client = None + + +class CustomAuthProvider(AuthProvider): + """Custom authentication provider that uses an external endpoint.""" + + def __init__(self, config: dict[str, str]): + self.endpoint = config["endpoint"] + self._client = None + + async def validate_token(self, token: str, scope: dict | None = None) -> AccessAttributes | None: + """Validate a token using the custom authentication endpoint.""" + if not self.endpoint: + raise ValueError("Authentication endpoint not configured") + + if scope is None: + scope = {} + + headers = dict(scope.get("headers", [])) + path = scope.get("path", "") + request_headers = {k.decode(): v.decode() for k, v in headers.items()} + + # Remove sensitive headers + if "authorization" in request_headers: + del request_headers["authorization"] + + query_string = scope.get("query_string", b"").decode() + params = parse_qs(query_string) + + # Build the auth request model + auth_request = AuthRequest( + api_key=token, + request=AuthRequestContext( + path=path, + headers=request_headers, + params=params, + ), + ) + + # Validate with authentication endpoint + try: + async with httpx.AsyncClient() as client: + response = await client.post( + self.endpoint, + json=auth_request.model_dump(), + timeout=10.0, # Add a reasonable timeout + ) + if response.status_code != 200: + logger.warning(f"Authentication failed with status code: {response.status_code}") + raise ValueError(f"Authentication failed: {response.status_code}") + + # Parse and validate the auth response + try: + response_data = response.json() + auth_response = AuthResponse(**response_data) + + # Store attributes in request scope for access control + if auth_response.access_attributes: + return auth_response.access_attributes + else: + logger.warning("No access attributes, setting namespace to api_key by default") + user_attributes = { + "namespaces": [token], + } + + scope["user_attributes"] = user_attributes + logger.debug(f"Authentication successful: {len(user_attributes)} attributes") + return auth_response.access_attributes + except Exception as e: + logger.exception("Error parsing authentication response") + raise ValueError("Invalid authentication response format") from e + + except httpx.TimeoutException: + logger.exception("Authentication request timed out") + raise + except ValueError: + # Re-raise ValueError exceptions to preserve their message + raise + except Exception as e: + logger.exception("Error during authentication") + raise ValueError("Authentication service error") from e + + async def close(self): + """Close the HTTP client.""" + if self._client: + await self._client.aclose() + self._client = None + + +def create_auth_provider(config: AuthProviderConfig) -> AuthProvider: + """Factory function to create the appropriate auth provider.""" + provider_type = config.provider_type.lower() + + if provider_type == "kubernetes": + return KubernetesAuthProvider(config.config) + elif provider_type == "custom": + return CustomAuthProvider(config.config) + else: + supported_providers = ", ".join([t.value for t in AuthProviderType]) + raise ValueError(f"Unsupported auth provider type: {provider_type}. Supported types are: {supported_providers}") diff --git a/llama_stack/distribution/server/endpoints.py b/llama_stack/distribution/server/endpoints.py index 98f01c067..ec1f7e083 100644 --- a/llama_stack/distribution/server/endpoints.py +++ b/llama_stack/distribution/server/endpoints.py @@ -6,7 +6,6 @@ import inspect import re -from typing import Dict, List from pydantic import BaseModel @@ -29,7 +28,7 @@ def toolgroup_protocol_map(): } -def get_all_api_endpoints() -> Dict[Api, List[ApiEndpoint]]: +def get_all_api_endpoints() -> dict[Api, list[ApiEndpoint]]: apis = {} protocols = api_protocol_map() diff --git a/llama_stack/distribution/server/server.py b/llama_stack/distribution/server/server.py index d7ef37c26..0c8c70306 100644 --- a/llama_stack/distribution/server/server.py +++ b/llama_stack/distribution/server/server.py @@ -15,15 +15,15 @@ import warnings from contextlib import asynccontextmanager from importlib.metadata import version as parse_version from pathlib import Path -from typing import Any, List, Optional, Union +from typing import Annotated, Any import yaml from fastapi import Body, FastAPI, HTTPException, Request from fastapi import Path as FastapiPath from fastapi.exceptions import RequestValidationError from fastapi.responses import JSONResponse, StreamingResponse +from openai import BadRequestError from pydantic import BaseModel, ValidationError -from typing_extensions import Annotated from llama_stack.distribution.datatypes import LoggingConfig, StackRunConfig from llama_stack.distribution.distribution import builtin_automatically_routed_apis @@ -90,9 +90,9 @@ async def global_exception_handler(request: Request, exc: Exception): return JSONResponse(status_code=http_exc.status_code, content={"error": {"detail": http_exc.detail}}) -def translate_exception(exc: Exception) -> Union[HTTPException, RequestValidationError]: +def translate_exception(exc: Exception) -> HTTPException | RequestValidationError: if isinstance(exc, ValidationError): - exc = RequestValidationError(exc.raw_errors) + exc = RequestValidationError(exc.errors()) if isinstance(exc, RequestValidationError): return HTTPException( @@ -110,6 +110,8 @@ def translate_exception(exc: Exception) -> Union[HTTPException, RequestValidatio ) elif isinstance(exc, ValueError): return HTTPException(status_code=400, detail=f"Invalid value: {str(exc)}") + elif isinstance(exc, BadRequestError): + return HTTPException(status_code=400, detail=str(exc)) elif isinstance(exc, PermissionError): return HTTPException(status_code=403, detail=f"Permission denied: {str(exc)}") elif isinstance(exc, TimeoutError): @@ -162,14 +164,17 @@ async def maybe_await(value): return value -async def sse_generator(event_gen): +async def sse_generator(event_gen_coroutine): + event_gen = None try: - async for item in await event_gen: + event_gen = await event_gen_coroutine + async for item in event_gen: yield create_sse_event(item) await asyncio.sleep(0.01) except asyncio.CancelledError: logger.info("Generator cancelled") - await event_gen.aclose() + if event_gen: + await event_gen.aclose() except Exception as e: logger.exception("Error in sse_generator") yield create_sse_event( @@ -229,15 +234,30 @@ class TracingMiddleware: def __init__(self, app, impls): self.app = app self.impls = impls + # FastAPI built-in paths that should bypass custom routing + self.fastapi_paths = ("/docs", "/redoc", "/openapi.json", "/favicon.ico", "/static") async def __call__(self, scope, receive, send): if scope.get("type") == "lifespan": return await self.app(scope, receive, send) path = scope.get("path", "") + + # Check if the path is a FastAPI built-in path + if path.startswith(self.fastapi_paths): + # Pass through to FastAPI's built-in handlers + logger.debug(f"Bypassing custom routing for FastAPI built-in path: {path}") + return await self.app(scope, receive, send) + if not hasattr(self, "endpoint_impls"): self.endpoint_impls = initialize_endpoint_impls(self.impls) - _, _, trace_path = find_matching_endpoint(scope.get("method", "GET"), path, self.endpoint_impls) + + try: + _, _, trace_path = find_matching_endpoint(scope.get("method", "GET"), path, self.endpoint_impls) + except ValueError: + # If no matching endpoint is found, pass through to FastAPI + logger.debug(f"No matching endpoint found for path: {path}, falling back to FastAPI") + return await self.app(scope, receive, send) trace_context = await start_trace(trace_path, {"__location__": "server", "raw_path": path}) @@ -294,7 +314,7 @@ class ClientVersionMiddleware: return await self.app(scope, receive, send) -def main(args: Optional[argparse.Namespace] = None): +def main(args: argparse.Namespace | None = None): """Start the LlamaStack server.""" parser = argparse.ArgumentParser(description="Start the LlamaStack server.") parser.add_argument( @@ -364,7 +384,7 @@ def main(args: Optional[argparse.Namespace] = None): raise ValueError("Either --yaml-config or --template must be provided") logger_config = None - with open(config_file, "r") as fp: + with open(config_file) as fp: config_contents = yaml.safe_load(fp) if isinstance(config_contents, dict) and (cfg := config_contents.get("logging_config")): logger_config = LoggingConfig(**cfg) @@ -388,14 +408,19 @@ def main(args: Optional[argparse.Namespace] = None): safe_config = redact_sensitive_fields(config.model_dump()) logger.info(yaml.dump(safe_config, indent=2)) - app = FastAPI(lifespan=lifespan) + app = FastAPI( + lifespan=lifespan, + docs_url="/docs", + redoc_url="/redoc", + openapi_url="/openapi.json", + ) if not os.environ.get("LLAMA_STACK_DISABLE_VERSION_CHECK"): app.add_middleware(ClientVersionMiddleware) # Add authentication middleware if configured - if config.server.auth and config.server.auth.endpoint: - logger.info(f"Enabling authentication with endpoint: {config.server.auth.endpoint}") - app.add_middleware(AuthenticationMiddleware, auth_endpoint=config.server.auth.endpoint) + if config.server.auth: + logger.info(f"Enabling authentication with provider: {config.server.auth.provider_type.value}") + app.add_middleware(AuthenticationMiddleware, auth_config=config.server.auth) try: impls = asyncio.run(construct_stack(config)) @@ -435,6 +460,7 @@ def main(args: Optional[argparse.Namespace] = None): raise ValueError(f"Could not find method {endpoint.name} on {impl}!!") impl_method = getattr(impl, endpoint.name) + logger.debug(f"{endpoint.method.upper()} {endpoint.route}") with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=UserWarning, module="pydantic._internal._fields") @@ -490,7 +516,7 @@ def main(args: Optional[argparse.Namespace] = None): uvicorn.run(**uvicorn_config) -def extract_path_params(route: str) -> List[str]: +def extract_path_params(route: str) -> list[str]: segments = route.split("/") params = [seg[1:-1] for seg in segments if seg.startswith("{") and seg.endswith("}")] # to handle path params like {param:path} diff --git a/llama_stack/distribution/stack.py b/llama_stack/distribution/stack.py index a6dc3d2a0..fc68dc016 100644 --- a/llama_stack/distribution/stack.py +++ b/llama_stack/distribution/stack.py @@ -8,7 +8,7 @@ import importlib.resources import os import re import tempfile -from typing import Any, Dict, Optional +from typing import Any import yaml @@ -90,7 +90,7 @@ RESOURCES = [ ] -async def register_resources(run_config: StackRunConfig, impls: Dict[Api, Any]): +async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]): for rsrc, api, register_method, list_method in RESOURCES: objects = getattr(run_config, rsrc) if api not in impls: @@ -197,7 +197,7 @@ def validate_env_pair(env_pair: str) -> tuple[str, str]: ) from e -def add_internal_implementations(impls: Dict[Api, Any], run_config: StackRunConfig) -> None: +def add_internal_implementations(impls: dict[Api, Any], run_config: StackRunConfig) -> None: """Add internal implementations (inspect and providers) to the implementations dictionary. Args: @@ -220,8 +220,8 @@ def add_internal_implementations(impls: Dict[Api, Any], run_config: StackRunConf # Produces a stack of providers for the given run config. Not all APIs may be # asked for in the run config. async def construct_stack( - run_config: StackRunConfig, provider_registry: Optional[ProviderRegistry] = None -) -> Dict[Api, Any]: + run_config: StackRunConfig, provider_registry: ProviderRegistry | None = None +) -> dict[Api, Any]: dist_registry, _ = await create_dist_registry(run_config.metadata_store, run_config.image_name) impls = await resolve_impls(run_config, provider_registry or get_provider_registry(run_config), dist_registry) @@ -244,7 +244,7 @@ def get_stack_run_config_from_template(template: str) -> StackRunConfig: def run_config_from_adhoc_config_spec( - adhoc_config_spec: str, provider_registry: Optional[ProviderRegistry] = None + adhoc_config_spec: str, provider_registry: ProviderRegistry | None = None ) -> StackRunConfig: """ Create an adhoc distribution from a list of API providers. diff --git a/llama_stack/distribution/store/registry.py b/llama_stack/distribution/store/registry.py index 76b66cc7a..ae97f600c 100644 --- a/llama_stack/distribution/store/registry.py +++ b/llama_stack/distribution/store/registry.py @@ -6,7 +6,7 @@ import asyncio from contextlib import asynccontextmanager -from typing import Dict, List, Optional, Protocol, Tuple +from typing import Protocol import pydantic @@ -20,13 +20,13 @@ logger = get_logger(__name__, category="core") class DistributionRegistry(Protocol): - async def get_all(self) -> List[RoutableObjectWithProvider]: ... + async def get_all(self) -> list[RoutableObjectWithProvider]: ... async def initialize(self) -> None: ... - async def get(self, identifier: str) -> Optional[RoutableObjectWithProvider]: ... + async def get(self, identifier: str) -> RoutableObjectWithProvider | None: ... - def get_cached(self, identifier: str) -> Optional[RoutableObjectWithProvider]: ... + def get_cached(self, identifier: str) -> RoutableObjectWithProvider | None: ... async def update(self, obj: RoutableObjectWithProvider) -> RoutableObjectWithProvider: ... @@ -40,13 +40,13 @@ KEY_VERSION = "v8" KEY_FORMAT = f"{REGISTER_PREFIX}:{KEY_VERSION}::" + "{type}:{identifier}" -def _get_registry_key_range() -> Tuple[str, str]: +def _get_registry_key_range() -> tuple[str, str]: """Returns the start and end keys for the registry range query.""" start_key = f"{REGISTER_PREFIX}:{KEY_VERSION}" return start_key, f"{start_key}\xff" -def _parse_registry_values(values: List[str]) -> List[RoutableObjectWithProvider]: +def _parse_registry_values(values: list[str]) -> list[RoutableObjectWithProvider]: """Utility function to parse registry values into RoutableObjectWithProvider objects.""" all_objects = [] for value in values: @@ -67,16 +67,16 @@ class DiskDistributionRegistry(DistributionRegistry): async def initialize(self) -> None: pass - def get_cached(self, type: str, identifier: str) -> Optional[RoutableObjectWithProvider]: + def get_cached(self, type: str, identifier: str) -> RoutableObjectWithProvider | None: # Disk registry does not have a cache raise NotImplementedError("Disk registry does not have a cache") - async def get_all(self) -> List[RoutableObjectWithProvider]: + async def get_all(self) -> list[RoutableObjectWithProvider]: start_key, end_key = _get_registry_key_range() values = await self.kvstore.range(start_key, end_key) return _parse_registry_values(values) - async def get(self, type: str, identifier: str) -> Optional[RoutableObjectWithProvider]: + async def get(self, type: str, identifier: str) -> RoutableObjectWithProvider | None: json_str = await self.kvstore.get(KEY_FORMAT.format(type=type, identifier=identifier)) if not json_str: return None @@ -113,7 +113,7 @@ class DiskDistributionRegistry(DistributionRegistry): class CachedDiskDistributionRegistry(DiskDistributionRegistry): def __init__(self, kvstore: KVStore): super().__init__(kvstore) - self.cache: Dict[Tuple[str, str], RoutableObjectWithProvider] = {} + self.cache: dict[tuple[str, str], RoutableObjectWithProvider] = {} self._initialized = False self._initialize_lock = asyncio.Lock() self._cache_lock = asyncio.Lock() @@ -147,15 +147,15 @@ class CachedDiskDistributionRegistry(DiskDistributionRegistry): async def initialize(self) -> None: await self._ensure_initialized() - def get_cached(self, type: str, identifier: str) -> Optional[RoutableObjectWithProvider]: + def get_cached(self, type: str, identifier: str) -> RoutableObjectWithProvider | None: return self.cache.get((type, identifier), None) - async def get_all(self) -> List[RoutableObjectWithProvider]: + async def get_all(self) -> list[RoutableObjectWithProvider]: await self._ensure_initialized() async with self._locked_cache() as cache: return list(cache.values()) - async def get(self, type: str, identifier: str) -> Optional[RoutableObjectWithProvider]: + async def get(self, type: str, identifier: str) -> RoutableObjectWithProvider | None: await self._ensure_initialized() cache_key = (type, identifier) @@ -189,7 +189,7 @@ class CachedDiskDistributionRegistry(DiskDistributionRegistry): async def create_dist_registry( - metadata_store: Optional[KVStoreConfig], + metadata_store: KVStoreConfig | None, image_name: str, ) -> tuple[CachedDiskDistributionRegistry, KVStore]: # instantiate kvstore for storing and retrieving distribution metadata diff --git a/llama_stack/distribution/ui/modules/api.py b/llama_stack/distribution/ui/modules/api.py index d5395c5b9..11455ed46 100644 --- a/llama_stack/distribution/ui/modules/api.py +++ b/llama_stack/distribution/ui/modules/api.py @@ -5,7 +5,6 @@ # the root directory of this source tree. import os -from typing import Optional from llama_stack_client import LlamaStackClient @@ -23,7 +22,7 @@ class LlamaStackApi: }, ) - def run_scoring(self, row, scoring_function_ids: list[str], scoring_params: Optional[dict]): + def run_scoring(self, row, scoring_function_ids: list[str], scoring_params: dict | None): """Run scoring on a single row""" if not scoring_params: scoring_params = {fn_id: None for fn_id in scoring_function_ids} diff --git a/llama_stack/distribution/ui/page/playground/rag.py b/llama_stack/distribution/ui/page/playground/rag.py index 392c9afe2..696d89bc2 100644 --- a/llama_stack/distribution/ui/page/playground/rag.py +++ b/llama_stack/distribution/ui/page/playground/rag.py @@ -24,6 +24,13 @@ def rag_chat_page(): def should_disable_input(): return "displayed_messages" in st.session_state and len(st.session_state.displayed_messages) > 0 + def log_message(message): + with st.chat_message(message["role"]): + if "tool_output" in message and message["tool_output"]: + with st.expander(label="Tool Output", expanded=False, icon="🛠"): + st.write(message["tool_output"]) + st.markdown(message["content"]) + with st.sidebar: # File/Directory Upload Section st.subheader("Upload Documents", divider=True) @@ -146,8 +153,7 @@ def rag_chat_page(): # Display chat history for message in st.session_state.displayed_messages: - with st.chat_message(message["role"]): - st.markdown(message["content"]) + log_message(message) if temperature > 0.0: strategy = { @@ -201,7 +207,7 @@ def rag_chat_page(): # Display assistant response with st.chat_message("assistant"): - retrieval_message_placeholder = st.empty() + retrieval_message_placeholder = st.expander(label="Tool Output", expanded=False, icon="🛠") message_placeholder = st.empty() full_response = "" retrieval_response = "" @@ -209,14 +215,16 @@ def rag_chat_page(): log.print() if log.role == "tool_execution": retrieval_response += log.content.replace("====", "").strip() - retrieval_message_placeholder.info(retrieval_response) + retrieval_message_placeholder.write(retrieval_response) else: full_response += log.content message_placeholder.markdown(full_response + "▌") message_placeholder.markdown(full_response) st.session_state.messages.append({"role": "assistant", "content": full_response}) - st.session_state.displayed_messages.append({"role": "assistant", "content": full_response}) + st.session_state.displayed_messages.append( + {"role": "assistant", "content": full_response, "tool_output": retrieval_response} + ) def direct_process_prompt(prompt): # Add the system prompt in the beginning of the conversation @@ -230,15 +238,14 @@ def rag_chat_page(): prompt_context = rag_response.content with st.chat_message("assistant"): + with st.expander(label="Retrieval Output", expanded=False): + st.write(prompt_context) + retrieval_message_placeholder = st.empty() message_placeholder = st.empty() full_response = "" retrieval_response = "" - # Display the retrieved content - retrieval_response += str(prompt_context) - retrieval_message_placeholder.info(retrieval_response) - # Construct the extended prompt extended_prompt = f"Please answer the following query using the context below.\n\nCONTEXT:\n{prompt_context}\n\nQUERY:\n{prompt}" diff --git a/llama_stack/distribution/ui/page/playground/tools.py b/llama_stack/distribution/ui/page/playground/tools.py index e987f617b..149d8cce9 100644 --- a/llama_stack/distribution/ui/page/playground/tools.py +++ b/llama_stack/distribution/ui/page/playground/tools.py @@ -4,14 +4,23 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import enum +import json import uuid import streamlit as st from llama_stack_client import Agent +from llama_stack_client.lib.agents.react.agent import ReActAgent +from llama_stack_client.lib.agents.react.tool_parser import ReActOutput from llama_stack.distribution.ui.modules.api import llama_stack_api +class AgentType(enum.Enum): + REGULAR = "Regular" + REACT = "ReAct" + + def tool_chat_page(): st.title("🛠 Tools") @@ -23,50 +32,121 @@ def tool_chat_page(): tool_groups_list = [tool_group.identifier for tool_group in tool_groups] mcp_tools_list = [tool for tool in tool_groups_list if tool.startswith("mcp::")] builtin_tools_list = [tool for tool in tool_groups_list if not tool.startswith("mcp::")] + selected_vector_dbs = [] def reset_agent(): st.session_state.clear() st.cache_resource.clear() with st.sidebar: + st.title("Configuration") st.subheader("Model") - model = st.selectbox(label="models", options=model_list, on_change=reset_agent) + model = st.selectbox(label="Model", options=model_list, on_change=reset_agent, label_visibility="collapsed") + + st.subheader("Available ToolGroups") - st.subheader("Builtin Tools") toolgroup_selection = st.pills( - label="Available ToolGroups", options=builtin_tools_list, selection_mode="multi", on_change=reset_agent + label="Built-in tools", + options=builtin_tools_list, + selection_mode="multi", + on_change=reset_agent, + format_func=lambda tool: "".join(tool.split("::")[1:]), + help="List of built-in tools from your llama stack server.", ) - st.subheader("MCP Servers") + if "builtin::rag" in toolgroup_selection: + vector_dbs = llama_stack_api.client.vector_dbs.list() or [] + if not vector_dbs: + st.info("No vector databases available for selection.") + vector_dbs = [vector_db.identifier for vector_db in vector_dbs] + selected_vector_dbs = st.multiselect( + label="Select Document Collections to use in RAG queries", + options=vector_dbs, + on_change=reset_agent, + ) + mcp_selection = st.pills( - label="Available MCP Servers", options=mcp_tools_list, selection_mode="multi", on_change=reset_agent + label="MCP Servers", + options=mcp_tools_list, + selection_mode="multi", + on_change=reset_agent, + format_func=lambda tool: "".join(tool.split("::")[1:]), + help="List of MCP servers registered to your llama stack server.", ) toolgroup_selection.extend(mcp_selection) - active_tool_list = [] - for toolgroup_id in toolgroup_selection: - active_tool_list.extend( - [ - f"{''.join(toolgroup_id.split('::')[1:])}:{t.identifier}" - for t in client.tools.list(toolgroup_id=toolgroup_id) - ] - ) + grouped_tools = {} + total_tools = 0 - st.subheader(f"Active Tools: 🛠 {len(active_tool_list)}") - st.json(active_tool_list) + for toolgroup_id in toolgroup_selection: + tools = client.tools.list(toolgroup_id=toolgroup_id) + grouped_tools[toolgroup_id] = [tool.identifier for tool in tools] + total_tools += len(tools) + + st.markdown(f"Active Tools: 🛠 {total_tools}") + + for group_id, tools in grouped_tools.items(): + with st.expander(f"🔧 Tools from `{group_id}`"): + for idx, tool in enumerate(tools, start=1): + st.markdown(f"{idx}. `{tool.split(':')[-1]}`") + + st.subheader("Agent Configurations") + st.subheader("Agent Type") + agent_type = st.radio( + label="Select Agent Type", + options=["Regular", "ReAct"], + on_change=reset_agent, + ) + + if agent_type == "ReAct": + agent_type = AgentType.REACT + else: + agent_type = AgentType.REGULAR + + max_tokens = st.slider( + "Max Tokens", + min_value=0, + max_value=4096, + value=512, + step=64, + help="The maximum number of tokens to generate", + on_change=reset_agent, + ) + + for i, tool_name in enumerate(toolgroup_selection): + if tool_name == "builtin::rag": + tool_dict = dict( + name="builtin::rag", + args={ + "vector_db_ids": list(selected_vector_dbs), + }, + ) + toolgroup_selection[i] = tool_dict @st.cache_resource def create_agent(): - return Agent( - client, - model=model, - instructions="You are a helpful assistant. When you use a tool always respond with a summary of the result.", - tools=toolgroup_selection, - sampling_params={ - "strategy": {"type": "greedy"}, - }, - ) + if "agent_type" in st.session_state and st.session_state.agent_type == AgentType.REACT: + return ReActAgent( + client=client, + model=model, + tools=toolgroup_selection, + response_format={ + "type": "json_schema", + "json_schema": ReActOutput.model_json_schema(), + }, + sampling_params={"strategy": {"type": "greedy"}, "max_tokens": max_tokens}, + ) + else: + return Agent( + client, + model=model, + instructions="You are a helpful assistant. When you use a tool always respond with a summary of the result.", + tools=toolgroup_selection, + sampling_params={"strategy": {"type": "greedy"}, "max_tokens": max_tokens}, + ) + + st.session_state.agent_type = agent_type agent = create_agent() @@ -95,6 +175,158 @@ def tool_chat_page(): ) def response_generator(turn_response): + if st.session_state.get("agent_type") == AgentType.REACT: + return _handle_react_response(turn_response) + else: + return _handle_regular_response(turn_response) + + def _handle_react_response(turn_response): + current_step_content = "" + final_answer = None + tool_results = [] + + for response in turn_response: + if not hasattr(response.event, "payload"): + yield ( + "\n\n🚨 :red[_Llama Stack server Error:_]\n" + "The response received is missing an expected `payload` attribute.\n" + "This could indicate a malformed response or an internal issue within the server.\n\n" + f"Error details: {response}" + ) + return + + payload = response.event.payload + + if payload.event_type == "step_progress" and hasattr(payload.delta, "text"): + current_step_content += payload.delta.text + continue + + if payload.event_type == "step_complete": + step_details = payload.step_details + + if step_details.step_type == "inference": + yield from _process_inference_step(current_step_content, tool_results, final_answer) + current_step_content = "" + elif step_details.step_type == "tool_execution": + tool_results = _process_tool_execution(step_details, tool_results) + current_step_content = "" + else: + current_step_content = "" + + if not final_answer and tool_results: + yield from _format_tool_results_summary(tool_results) + + def _process_inference_step(current_step_content, tool_results, final_answer): + try: + react_output_data = json.loads(current_step_content) + thought = react_output_data.get("thought") + action = react_output_data.get("action") + answer = react_output_data.get("answer") + + if answer and answer != "null" and answer is not None: + final_answer = answer + + if thought: + with st.expander("🤔 Thinking...", expanded=False): + st.markdown(f":grey[__{thought}__]") + + if action and isinstance(action, dict): + tool_name = action.get("tool_name") + tool_params = action.get("tool_params") + with st.expander(f'🛠 Action: Using tool "{tool_name}"', expanded=False): + st.json(tool_params) + + if answer and answer != "null" and answer is not None: + yield f"\n\n✅ **Final Answer:**\n{answer}" + + except json.JSONDecodeError: + yield f"\n\nFailed to parse ReAct step content:\n```json\n{current_step_content}\n```" + except Exception as e: + yield f"\n\nFailed to process ReAct step: {e}\n```json\n{current_step_content}\n```" + + return final_answer + + def _process_tool_execution(step_details, tool_results): + try: + if hasattr(step_details, "tool_responses") and step_details.tool_responses: + for tool_response in step_details.tool_responses: + tool_name = tool_response.tool_name + content = tool_response.content + tool_results.append((tool_name, content)) + with st.expander(f'⚙️ Observation (Result from "{tool_name}")', expanded=False): + try: + parsed_content = json.loads(content) + st.json(parsed_content) + except json.JSONDecodeError: + st.code(content, language=None) + else: + with st.expander("⚙️ Observation", expanded=False): + st.markdown(":grey[_Tool execution step completed, but no response data found._]") + except Exception as e: + with st.expander("⚙️ Error in Tool Execution", expanded=False): + st.markdown(f":red[_Error processing tool execution: {str(e)}_]") + + return tool_results + + def _format_tool_results_summary(tool_results): + yield "\n\n**Here's what I found:**\n" + for tool_name, content in tool_results: + try: + parsed_content = json.loads(content) + + if tool_name == "web_search" and "top_k" in parsed_content: + yield from _format_web_search_results(parsed_content) + elif "results" in parsed_content and isinstance(parsed_content["results"], list): + yield from _format_results_list(parsed_content["results"]) + elif isinstance(parsed_content, dict) and len(parsed_content) > 0: + yield from _format_dict_results(parsed_content) + elif isinstance(parsed_content, list) and len(parsed_content) > 0: + yield from _format_list_results(parsed_content) + except json.JSONDecodeError: + yield f"\n**{tool_name}** was used but returned complex data. Check the observation for details.\n" + except (TypeError, AttributeError, KeyError, IndexError) as e: + print(f"Error processing {tool_name} result: {type(e).__name__}: {e}") + + def _format_web_search_results(parsed_content): + for i, result in enumerate(parsed_content["top_k"], 1): + if i <= 3: + title = result.get("title", "Untitled") + url = result.get("url", "") + content_text = result.get("content", "").strip() + yield f"\n- **{title}**\n {content_text}\n [Source]({url})\n" + + def _format_results_list(results): + for i, result in enumerate(results, 1): + if i <= 3: + if isinstance(result, dict): + name = result.get("name", result.get("title", "Result " + str(i))) + description = result.get("description", result.get("content", result.get("summary", ""))) + yield f"\n- **{name}**\n {description}\n" + else: + yield f"\n- {result}\n" + + def _format_dict_results(parsed_content): + yield "\n```\n" + for key, value in list(parsed_content.items())[:5]: + if isinstance(value, str) and len(value) < 100: + yield f"{key}: {value}\n" + else: + yield f"{key}: [Complex data]\n" + yield "```\n" + + def _format_list_results(parsed_content): + yield "\n" + for _, item in enumerate(parsed_content[:3], 1): + if isinstance(item, str): + yield f"- {item}\n" + elif isinstance(item, dict) and "text" in item: + yield f"- {item['text']}\n" + elif isinstance(item, dict) and len(item) > 0: + first_value = next(iter(item.values())) + if isinstance(first_value, str) and len(first_value) < 100: + yield f"- {first_value}\n" + + def _handle_regular_response(turn_response): for response in turn_response: if hasattr(response.event, "payload"): print(response.event.payload) @@ -103,14 +335,18 @@ def tool_chat_page(): yield response.event.payload.delta.text if response.event.payload.event_type == "step_complete": if response.event.payload.step_details.step_type == "tool_execution": - yield " 🛠 " + if response.event.payload.step_details.tool_calls: + tool_name = str(response.event.payload.step_details.tool_calls[0].tool_name) + yield f'\n\n🛠 :grey[_Using "{tool_name}" tool:_]\n\n' + else: + yield "No tool_calls present in step_details" else: yield f"Error occurred in the Llama Stack Cluster: {response}" with st.chat_message("assistant"): - response = st.write_stream(response_generator(turn_response)) + response_content = st.write_stream(response_generator(turn_response)) - st.session_state.messages.append({"role": "assistant", "content": response}) + st.session_state.messages.append({"role": "assistant", "content": response_content}) tool_chat_page() diff --git a/llama_stack/distribution/ui/requirements.txt b/llama_stack/distribution/ui/requirements.txt index 61d42768d..53a1e7bf3 100644 --- a/llama_stack/distribution/ui/requirements.txt +++ b/llama_stack/distribution/ui/requirements.txt @@ -1,5 +1,5 @@ -streamlit -pandas -llama-stack-client>=0.2.1 -streamlit-option-menu llama-stack>=0.2.1 +llama-stack-client>=0.2.1 +pandas +streamlit +streamlit-option-menu diff --git a/llama_stack/distribution/utils/config.py b/llama_stack/distribution/utils/config.py index 5e78289b7..dece52460 100644 --- a/llama_stack/distribution/utils/config.py +++ b/llama_stack/distribution/utils/config.py @@ -4,10 +4,10 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any -def redact_sensitive_fields(data: Dict[str, Any]) -> Dict[str, Any]: +def redact_sensitive_fields(data: dict[str, Any]) -> dict[str, Any]: """Redact sensitive information from config before printing.""" sensitive_patterns = ["api_key", "api_token", "password", "secret"] @@ -18,7 +18,7 @@ def redact_sensitive_fields(data: Dict[str, Any]) -> Dict[str, Any]: return [_redact_value(i) for i in v] return v - def _redact_dict(d: Dict[str, Any]) -> Dict[str, Any]: + def _redact_dict(d: dict[str, Any]) -> dict[str, Any]: result = {} for k, v in d.items(): if any(pattern in k.lower() for pattern in sensitive_patterns): diff --git a/llama_stack/distribution/utils/context.py b/llama_stack/distribution/utils/context.py index c34079ac6..3fcd3315f 100644 --- a/llama_stack/distribution/utils/context.py +++ b/llama_stack/distribution/utils/context.py @@ -4,14 +4,15 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +from collections.abc import AsyncGenerator from contextvars import ContextVar -from typing import AsyncGenerator, List, TypeVar +from typing import TypeVar T = TypeVar("T") def preserve_contexts_async_generator( - gen: AsyncGenerator[T, None], context_vars: List[ContextVar] + gen: AsyncGenerator[T, None], context_vars: list[ContextVar] ) -> AsyncGenerator[T, None]: """ Wraps an async generator to preserve context variables across iterations. diff --git a/llama_stack/distribution/utils/prompt_for_config.py b/llama_stack/distribution/utils/prompt_for_config.py index 9b2b99022..26f6920e0 100644 --- a/llama_stack/distribution/utils/prompt_for_config.py +++ b/llama_stack/distribution/utils/prompt_for_config.py @@ -8,12 +8,11 @@ import inspect import json import logging from enum import Enum -from typing import Any, List, Literal, Optional, Type, Union, get_args, get_origin +from typing import Annotated, Any, Literal, Union, get_args, get_origin from pydantic import BaseModel from pydantic.fields import FieldInfo from pydantic_core import PydanticUndefinedType -from typing_extensions import Annotated log = logging.getLogger(__name__) @@ -21,7 +20,7 @@ log = logging.getLogger(__name__) def is_list_of_primitives(field_type): """Check if a field type is a List of primitive types.""" origin = get_origin(field_type) - if origin is List or origin is list: + if origin is list or origin is list: args = get_args(field_type) if len(args) == 1 and args[0] in (int, float, str, bool): return True @@ -53,7 +52,7 @@ def get_non_none_type(field_type): return next(arg for arg in get_args(field_type) if arg is not type(None)) -def manually_validate_field(model: Type[BaseModel], field_name: str, value: Any): +def manually_validate_field(model: type[BaseModel], field_name: str, value: Any): validators = model.__pydantic_decorators__.field_validators for _name, validator in validators.items(): if field_name in validator.info.fields: @@ -126,7 +125,7 @@ def prompt_for_discriminated_union( # # doesn't support List[nested_class] yet or Dicts of any kind. needs a bunch of # unit tests for coverage. -def prompt_for_config(config_type: type[BaseModel], existing_config: Optional[BaseModel] = None) -> BaseModel: +def prompt_for_config(config_type: type[BaseModel], existing_config: BaseModel | None = None) -> BaseModel: """ Recursively prompt the user for configuration values based on a Pydantic BaseModel. diff --git a/llama_stack/log.py b/llama_stack/log.py index 3835b74a1..98858d208 100644 --- a/llama_stack/log.py +++ b/llama_stack/log.py @@ -7,7 +7,6 @@ import logging import os from logging.config import dictConfig -from typing import Dict, Optional from rich.console import Console from rich.errors import MarkupError @@ -33,7 +32,7 @@ CATEGORIES = [ ] # Initialize category levels with default level -_category_levels: Dict[str, int] = {category: DEFAULT_LOG_LEVEL for category in CATEGORIES} +_category_levels: dict[str, int] = {category: DEFAULT_LOG_LEVEL for category in CATEGORIES} def config_to_category_levels(category: str, level: str): @@ -49,7 +48,7 @@ def config_to_category_levels(category: str, level: str): Dict[str, int]: A dictionary mapping categories to their log levels. """ - category_levels: Dict[str, int] = {} + category_levels: dict[str, int] = {} level_value = logging._nameToLevel.get(str(level).upper()) if level_value is None: logging.warning(f"Unknown log level '{level}' for category '{category}'. Falling back to default 'INFO'.") @@ -69,7 +68,7 @@ def config_to_category_levels(category: str, level: str): return category_levels -def parse_yaml_config(yaml_config: LoggingConfig) -> Dict[str, int]: +def parse_yaml_config(yaml_config: LoggingConfig) -> dict[str, int]: """ Helper function to parse a yaml logging configuration found in the run.yaml @@ -86,7 +85,7 @@ def parse_yaml_config(yaml_config: LoggingConfig) -> Dict[str, int]: return category_levels -def parse_environment_config(env_config: str) -> Dict[str, int]: +def parse_environment_config(env_config: str) -> dict[str, int]: """ Parse the LLAMA_STACK_LOGGING environment variable and return a dictionary of category log levels. @@ -131,7 +130,7 @@ class CustomRichHandler(RichHandler): self.markup = original_markup -def setup_logging(category_levels: Dict[str, int], log_file: str | None) -> None: +def setup_logging(category_levels: dict[str, int], log_file: str | None) -> None: """ Configure logging based on the provided category log levels and an optional log file. @@ -211,7 +210,7 @@ def setup_logging(category_levels: Dict[str, int], log_file: str | None) -> None def get_logger( - name: str, category: str = "uncategorized", config: Optional[LoggingConfig] | None = None + name: str, category: str = "uncategorized", config: LoggingConfig | None | None = None ) -> logging.LoggerAdapter: """ Returns a logger with the specified name and category. diff --git a/llama_stack/models/llama/checkpoint.py b/llama_stack/models/llama/checkpoint.py index 2bae08a69..c9e0030e3 100644 --- a/llama_stack/models/llama/checkpoint.py +++ b/llama_stack/models/llama/checkpoint.py @@ -7,14 +7,14 @@ import concurrent.futures import re from pathlib import Path -from typing import Any, Dict, List, Optional, Union +from typing import Any import numpy as np import torch from fairscale.nn.model_parallel.initialize import get_model_parallel_rank, get_model_parallel_world_size -def map_mp_rank(old_mp_size: int, new_mp_size: int, new_mp_rank: int) -> List[int]: +def map_mp_rank(old_mp_size: int, new_mp_size: int, new_mp_rank: int) -> list[int]: """Map a new MP rank to a list of old MP ranks given a change in MP size.""" if new_mp_size % old_mp_size == 0: # Read old MP shard and split it into smaller ones @@ -31,12 +31,12 @@ def map_mp_rank(old_mp_size: int, new_mp_size: int, new_mp_rank: int) -> List[in def maybe_reshard_state_dict( - ckpt_paths: List[Path], + ckpt_paths: list[Path], n_kv_heads: int, - moe_num_experts: Optional[int] = None, - map_location: Union[str, torch.device] = "cpu", + moe_num_experts: int | None = None, + map_location: str | torch.device = "cpu", mmap: bool = True, -) -> Dict[str, torch.Tensor]: +) -> dict[str, torch.Tensor]: if str(map_location) == "cpu": torch.set_default_tensor_type(torch.BFloat16Tensor) else: @@ -97,18 +97,18 @@ _MOE_WEIGHT_COLUMN_KEY = {"feed_forward.experts.moe_w_out_eF_D"} def reshard_mp( - state_dicts: List[Dict[str, torch.Tensor]], + state_dicts: list[dict[str, torch.Tensor]], size: int, rank: int, repeat_qk_qv: int = 1, -) -> Dict[str, torch.Tensor]: +) -> dict[str, torch.Tensor]: """ Reshard a list of state dicts into a single state dict given a change in MP size. If the list has more than one state dict, we concatenate the values of the same key across all state dicts. Otherwise, we just slice it for the current MP rank. """ - def concat_or_chunk(tensors: List[torch.Tensor], dim: int) -> torch.Tensor: + def concat_or_chunk(tensors: list[torch.Tensor], dim: int) -> torch.Tensor: if len(tensors) > 1: return torch.cat(tensors, dim=dim) return tensors[0].chunk(size, dim=dim)[rank].clone() @@ -144,7 +144,7 @@ def reshard_mp( column_regex = re.compile("|".join(column_keys)) row_regex = re.compile("|".join(row_keys)) - output: Dict[str, torch.Tensor] = {} + output: dict[str, torch.Tensor] = {} with concurrent.futures.ThreadPoolExecutor() as executor: # Note: only processes keys in the first state dict. # Assumes keys are the same across all state dicts. @@ -154,7 +154,7 @@ def reshard_mp( return output -def convert_moe_weights(state_dict: Dict[str, Any], num_experts: int) -> Dict[str, Any]: +def convert_moe_weights(state_dict: dict[str, Any], num_experts: int) -> dict[str, Any]: routed_keys = _MOE_WEIGHT_ROW_KEY | _MOE_WEIGHT_COLUMN_KEY routed_regex = re.compile("|".join(routed_keys)) keys = list(state_dict.keys()) diff --git a/llama_stack/models/llama/datatypes.py b/llama_stack/models/llama/datatypes.py index 48cb51005..f9f094c3d 100644 --- a/llama_stack/models/llama/datatypes.py +++ b/llama_stack/models/llama/datatypes.py @@ -7,10 +7,9 @@ import base64 from enum import Enum from io import BytesIO -from typing import Any, Dict, List, Literal, Optional, Union +from typing import Annotated, Any, Literal from pydantic import BaseModel, ConfigDict, Field, field_serializer, field_validator -from typing_extensions import Annotated # The goal is that these set of types are relevant for all Llama models. # That isn't the current state yet -- e.g., BuiltinTool is somewhat specific to @@ -31,21 +30,21 @@ class BuiltinTool(Enum): code_interpreter = "code_interpreter" -Primitive = Union[str, int, float, bool, None] -RecursiveType = Union[Primitive, List[Primitive], Dict[str, Primitive]] +Primitive = str | int | float | bool | None +RecursiveType = Primitive | list[Primitive] | dict[str, Primitive] class ToolCall(BaseModel): call_id: str - tool_name: Union[BuiltinTool, str] + tool_name: BuiltinTool | str # Plan is to deprecate the Dict in favor of a JSON string # that is parsed on the client side instead of trying to manage # the recursive type here. # Making this a union so that client side can start prepping for this change. # Eventually, we will remove both the Dict and arguments_json field, # and arguments will just be a str - arguments: Union[str, Dict[str, RecursiveType]] - arguments_json: Optional[str] = None + arguments: str | dict[str, RecursiveType] + arguments_json: str | None = None @field_validator("tool_name", mode="before") @classmethod @@ -91,15 +90,15 @@ class StopReason(Enum): class ToolParamDefinition(BaseModel): param_type: str - description: Optional[str] = None - required: Optional[bool] = True - default: Optional[Any] = None + description: str | None = None + required: bool | None = True + default: Any | None = None class ToolDefinition(BaseModel): - tool_name: Union[BuiltinTool, str] - description: Optional[str] = None - parameters: Optional[Dict[str, ToolParamDefinition]] = None + tool_name: BuiltinTool | str + description: str | None = None + parameters: dict[str, ToolParamDefinition] | None = None @field_validator("tool_name", mode="before") @classmethod @@ -119,7 +118,7 @@ class RawMediaItem(BaseModel): model_config = ConfigDict(arbitrary_types_allowed=True) @field_serializer("data") - def serialize_data(self, data: Optional[bytes], _info): + def serialize_data(self, data: bytes | None, _info): if data is None: return None return base64.b64encode(data).decode("utf-8") @@ -137,9 +136,9 @@ class RawTextItem(BaseModel): text: str -RawContentItem = Annotated[Union[RawTextItem, RawMediaItem], Field(discriminator="type")] +RawContentItem = Annotated[RawTextItem | RawMediaItem, Field(discriminator="type")] -RawContent = str | RawContentItem | List[RawContentItem] +RawContent = str | RawContentItem | list[RawContentItem] class RawMessage(BaseModel): @@ -147,17 +146,17 @@ class RawMessage(BaseModel): content: RawContent # This is for RAG but likely should be absorbed into content - context: Optional[RawContent] = None + context: RawContent | None = None # These are for the output message coming from the assistant - stop_reason: Optional[StopReason] = None - tool_calls: List[ToolCall] = Field(default_factory=list) + stop_reason: StopReason | None = None + tool_calls: list[ToolCall] = Field(default_factory=list) class GenerationResult(BaseModel): token: int text: str - logprobs: Optional[List[float]] = None + logprobs: list[float] | None = None source: Literal["input"] | Literal["output"] diff --git a/llama_stack/models/llama/llama3/args.py b/llama_stack/models/llama/llama3/args.py index f7e4b4557..4f92874f5 100644 --- a/llama_stack/models/llama/llama3/args.py +++ b/llama_stack/models/llama/llama3/args.py @@ -6,7 +6,6 @@ from dataclasses import dataclass from enum import Enum -from typing import Optional class QuantizationScheme(Enum): @@ -15,8 +14,8 @@ class QuantizationScheme(Enum): @dataclass class QuantizationArgs: - scheme: Optional[QuantizationScheme] = None - group_size: Optional[int] = None + scheme: QuantizationScheme | None = None + group_size: int | None = None spinquant: bool = False def __init__(self, **kwargs): @@ -39,10 +38,10 @@ class ModelArgs: dim: int = 4096 n_layers: int = 32 n_heads: int = 32 - n_kv_heads: Optional[int] = None + n_kv_heads: int | None = None vocab_size: int = -1 multiple_of: int = 256 # make SwiGLU hidden layer size multiple of large power of 2 - ffn_dim_multiplier: Optional[float] = None + ffn_dim_multiplier: float | None = None norm_eps: float = 1e-5 rope_theta: float = 500000 use_scaled_rope: bool = False @@ -55,8 +54,8 @@ class ModelArgs: vision_max_num_chunks: int = 4 vision_num_cross_attention_layers: int = -1 - quantization_args: Optional[QuantizationArgs] = None - lora_args: Optional[LoRAArgs] = None + quantization_args: QuantizationArgs | None = None + lora_args: LoRAArgs | None = None def __init__(self, **kwargs): for k, v in kwargs.items(): diff --git a/llama_stack/models/llama/llama3/chat_format.py b/llama_stack/models/llama/llama3/chat_format.py index fe7a7a898..7bb05d8db 100644 --- a/llama_stack/models/llama/llama3/chat_format.py +++ b/llama_stack/models/llama/llama3/chat_format.py @@ -8,7 +8,6 @@ import io import json import uuid from dataclasses import dataclass -from typing import Dict, List, Optional, Tuple from PIL import Image as PIL_Image @@ -29,14 +28,14 @@ from .tool_utils import ToolUtils @dataclass class VisionInput: - mask: List[List[int]] - images: List[PIL_Image.Image] + mask: list[list[int]] + images: list[PIL_Image.Image] @dataclass class LLMInput: - tokens: List[int] - vision: Optional[VisionInput] = None + tokens: list[int] + vision: VisionInput | None = None def role_str(role: Role) -> str: @@ -50,7 +49,7 @@ def role_str(role: Role) -> str: class ChatFormat: - possible_headers: Dict[Role, str] + possible_headers: dict[Role, str] def __init__(self, tokenizer: Tokenizer): self.tokenizer = tokenizer @@ -58,7 +57,7 @@ class ChatFormat: self.possible_headers = {role: f"<|start_header_id|>{role_str(role)}<|end_header_id|>\n\n" for role in Role} self.vision_token = self.tokenizer.special_tokens["<|image|>"] - def _encode_header(self, role: str) -> List[int]: + def _encode_header(self, role: str) -> list[int]: tokens = [] tokens.append(self.tokenizer.special_tokens["<|start_header_id|>"]) tokens.extend(self.tokenizer.encode("ipython" if role == "tool" else role, bos=False, eos=False)) @@ -70,7 +69,7 @@ class ChatFormat: tokens, images = self._encode_content(content, bos=True) return self._model_input_from_tokens_images(tokens, images) - def _encode_content(self, content: RawContent, bos: bool = False) -> Tuple[List[int], List[PIL_Image.Image]]: + def _encode_content(self, content: RawContent, bos: bool = False) -> tuple[list[int], list[PIL_Image.Image]]: tokens = [] images = [] @@ -107,7 +106,7 @@ class ChatFormat: def encode_message( self, message: RawMessage, tool_prompt_format: ToolPromptFormat - ) -> Tuple[List[int], List[PIL_Image.Image]]: + ) -> tuple[list[int], list[PIL_Image.Image]]: tokens = self._encode_header(message.role) images = [] @@ -145,8 +144,8 @@ class ChatFormat: def encode_dialog_prompt( self, - messages: List[RawMessage], - tool_prompt_format: Optional[ToolPromptFormat] = None, + messages: list[RawMessage], + tool_prompt_format: ToolPromptFormat | None = None, ) -> LLMInput: tool_prompt_format = tool_prompt_format or ToolPromptFormat.json tokens = [] @@ -163,7 +162,7 @@ class ChatFormat: return self._model_input_from_tokens_images(tokens, images) # TODO(this should be generic, not only for assistant messages) - def decode_assistant_message(self, tokens: List[int], stop_reason: StopReason) -> RawMessage: + def decode_assistant_message(self, tokens: list[int], stop_reason: StopReason) -> RawMessage: content = self.tokenizer.decode(tokens) return self.decode_assistant_message_from_content(content, stop_reason) @@ -234,7 +233,7 @@ class ChatFormat: tool_calls=tool_calls, ) - def _model_input_from_tokens_images(self, tokens: List[int], images: List[PIL_Image.Image]) -> LLMInput: + def _model_input_from_tokens_images(self, tokens: list[int], images: list[PIL_Image.Image]) -> LLMInput: vision_input = None if len(images) > 0: vision_input = VisionInput( @@ -249,9 +248,9 @@ class ChatFormat: def create_vision_mask( - tokens: List[int], + tokens: list[int], vision_token: int, -) -> List[List[int]]: +) -> list[list[int]]: vision_token_locations = [i for i, token in enumerate(tokens) if token == vision_token] if len(vision_token_locations) == 0: return [] diff --git a/llama_stack/models/llama/llama3/generation.py b/llama_stack/models/llama/llama3/generation.py index 35c140707..c6d618818 100644 --- a/llama_stack/models/llama/llama3/generation.py +++ b/llama_stack/models/llama/llama3/generation.py @@ -15,8 +15,8 @@ import json import os import sys import time +from collections.abc import Callable, Generator from pathlib import Path -from typing import Callable, Generator, List, Optional import torch import torch.nn.functional as F @@ -41,8 +41,8 @@ class Llama3: ckpt_dir: str, max_seq_len: int, max_batch_size: int, - world_size: Optional[int] = None, - quantization_mode: Optional[QuantizationMode] = None, + world_size: int | None = None, + quantization_mode: QuantizationMode | None = None, seed: int = 1, device: str = "cuda", ): @@ -82,7 +82,7 @@ class Llama3: ckpt_paths = sorted(Path(ckpt_dir).glob("*.pth")) assert len(ckpt_paths) > 0, f"no checkpoint files found in {ckpt_dir}" print(f"Loading a checkpoint (shards={len(ckpt_paths)}, current-mp-size={world_size})") - with open(Path(ckpt_dir) / "params.json", "r") as f: + with open(Path(ckpt_dir) / "params.json") as f: params = json.loads(f.read()) model_args: ModelArgs = ModelArgs( @@ -154,15 +154,15 @@ class Llama3: @torch.inference_mode() def generate( self, - llm_inputs: List[LLMInput], + llm_inputs: list[LLMInput], temperature: float = 0.6, top_p: float = 0.9, - max_gen_len: Optional[int] = None, + max_gen_len: int | None = None, logprobs: bool = False, echo: bool = False, print_model_input: bool = False, - logits_processor: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, - ) -> Generator[List[GenerationResult], None, None]: + logits_processor: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = None, + ) -> Generator[list[GenerationResult], None, None]: if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.args.max_seq_len: max_gen_len = self.args.max_seq_len - 1 params = self.model.params @@ -302,13 +302,13 @@ class Llama3: def completion( self, - contents: List[RawContent], + contents: list[RawContent], temperature: float = 0.6, top_p: float = 0.9, - max_gen_len: Optional[int] = None, + max_gen_len: int | None = None, logprobs: bool = False, echo: bool = False, - ) -> Generator[List[GenerationResult], None, None]: + ) -> Generator[list[GenerationResult], None, None]: model_inputs = [self.formatter.encode_content(c) for c in contents] for result in self.generate( model_inputs=model_inputs, @@ -324,14 +324,14 @@ class Llama3: def chat_completion( self, - messages_batch: List[List[RawMessage]], + messages_batch: list[list[RawMessage]], temperature: float = 0.6, top_p: float = 0.9, - max_gen_len: Optional[int] = None, + max_gen_len: int | None = None, logprobs: bool = False, tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json, echo: bool = False, - ) -> Generator[List[GenerationResult], None, None]: + ) -> Generator[list[GenerationResult], None, None]: model_inputs = [self.formatter.encode_dialog_prompt(messages) for messages in messages_batch] for result in self.generate( model_inputs=model_inputs, diff --git a/llama_stack/models/llama/llama3/interface.py b/llama_stack/models/llama/llama3/interface.py index 8684237df..b63ba4847 100644 --- a/llama_stack/models/llama/llama3/interface.py +++ b/llama_stack/models/llama/llama3/interface.py @@ -12,7 +12,6 @@ # the top-level of this source tree. from pathlib import Path -from typing import List, Optional from termcolor import colored @@ -131,7 +130,7 @@ class LLama31Interface: self.formatter = ChatFormat(self.tokenizer) self.tool_prompt_format = tool_prompt_format - def get_tokens(self, messages: List[RawMessage]) -> List[int]: + def get_tokens(self, messages: list[RawMessage]) -> list[int]: model_input = self.formatter.encode_dialog_prompt( messages, self.tool_prompt_format, @@ -149,10 +148,10 @@ class LLama31Interface: def system_messages( self, - builtin_tools: List[BuiltinTool], - custom_tools: List[ToolDefinition], - instruction: Optional[str] = None, - ) -> List[RawMessage]: + builtin_tools: list[BuiltinTool], + custom_tools: list[ToolDefinition], + instruction: str | None = None, + ) -> list[RawMessage]: messages = [] default_gen = SystemDefaultGenerator() @@ -194,8 +193,8 @@ class LLama31Interface: self, content: str, stop_reason: StopReason, - tool_call: Optional[ToolCall] = None, - ) -> List[RawMessage]: + tool_call: ToolCall | None = None, + ) -> list[RawMessage]: tool_calls = [] if tool_call: tool_calls.append(tool_call) @@ -208,7 +207,7 @@ class LLama31Interface: ) ] - def user_message(self, content: str) -> List[RawMessage]: + def user_message(self, content: str) -> list[RawMessage]: return [RawMessage(role="user", content=content)] def display_message_as_tokens(self, message: RawMessage) -> None: @@ -228,7 +227,7 @@ class LLama31Interface: print("\n", end="") -def list_jinja_templates() -> List[Template]: +def list_jinja_templates() -> list[Template]: return TEMPLATES diff --git a/llama_stack/models/llama/llama3/model.py b/llama_stack/models/llama/llama3/model.py index 2562673e2..88f748c1d 100644 --- a/llama_stack/models/llama/llama3/model.py +++ b/llama_stack/models/llama/llama3/model.py @@ -5,7 +5,6 @@ # the root directory of this source tree. import math -from typing import Optional, Tuple import fairscale.nn.model_parallel.initialize as fs_init import torch @@ -80,7 +79,7 @@ def apply_rotary_emb( xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor, -) -> Tuple[torch.Tensor, torch.Tensor]: +) -> tuple[torch.Tensor, torch.Tensor]: xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) freqs_cis = reshape_for_broadcast(freqs_cis, xq_) @@ -162,7 +161,7 @@ class Attention(nn.Module): x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, - mask: Optional[torch.Tensor], + mask: torch.Tensor | None, ): bsz, seqlen, _ = x.shape xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) @@ -204,7 +203,7 @@ class FeedForward(nn.Module): dim: int, hidden_dim: int, multiple_of: int, - ffn_dim_multiplier: Optional[float], + ffn_dim_multiplier: float | None, ): super().__init__() hidden_dim = int(2 * hidden_dim / 3) @@ -243,7 +242,7 @@ class TransformerBlock(nn.Module): x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, - mask: Optional[torch.Tensor], + mask: torch.Tensor | None, ): h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask) out = h + self.feed_forward(self.ffn_norm(h)) diff --git a/llama_stack/models/llama/llama3/multimodal/image_transform.py b/llama_stack/models/llama/llama3/multimodal/image_transform.py index c156d6d2e..f2761ee47 100644 --- a/llama_stack/models/llama/llama3/multimodal/image_transform.py +++ b/llama_stack/models/llama/llama3/multimodal/image_transform.py @@ -14,7 +14,7 @@ import math from collections import defaultdict from logging import getLogger -from typing import Any, Optional, Set, Tuple +from typing import Any import torch import torchvision.transforms as tv @@ -26,7 +26,7 @@ IMAGE_RES = 224 logger = getLogger() -class VariableSizeImageTransform(object): +class VariableSizeImageTransform: """ This class accepts images of any size and dynamically resize, pads and chunks it based on the image aspect ratio and the number of image chunks we allow. @@ -75,7 +75,7 @@ class VariableSizeImageTransform(object): self.resample = tv.InterpolationMode.BILINEAR @staticmethod - def get_factors(n: int) -> Set[int]: + def get_factors(n: int) -> set[int]: """ Calculate all factors of a given number, i.e. a dividor that leaves no remainder. For example, if n=12, it will return {1, 2, 3, 4, 6, 12}. @@ -145,9 +145,9 @@ class VariableSizeImageTransform(object): @staticmethod def get_max_res_without_distortion( - image_size: Tuple[int, int], - target_size: Tuple[int, int], - ) -> Tuple[int, int]: + image_size: tuple[int, int], + target_size: tuple[int, int], + ) -> tuple[int, int]: """ Determines the maximum resolution to which an image can be resized to without distorting its aspect ratio, based on the target resolution. @@ -198,8 +198,8 @@ class VariableSizeImageTransform(object): def resize_without_distortion( self, image: torch.Tensor, - target_size: Tuple[int, int], - max_upscaling_size: Optional[int], + target_size: tuple[int, int], + max_upscaling_size: int | None, ) -> torch.Tensor: """ Used to resize an image to target_resolution, without distortion. @@ -261,10 +261,10 @@ class VariableSizeImageTransform(object): def get_best_fit( self, - image_size: Tuple[int, int], + image_size: tuple[int, int], possible_resolutions: torch.Tensor, resize_to_max_canvas: bool = False, - ) -> Tuple[int, int]: + ) -> tuple[int, int]: """ Determines the best canvas possible from a list of possible resolutions to, without distortion, resize an image to. @@ -364,7 +364,7 @@ class VariableSizeImageTransform(object): max_num_chunks: int, normalize_img: bool = True, resize_to_max_canvas: bool = False, - ) -> Tuple[Any, Any]: + ) -> tuple[Any, Any]: """ Args: image (PIL.Image): Image to be resized. diff --git a/llama_stack/models/llama/llama3/multimodal/model.py b/llama_stack/models/llama/llama3/multimodal/model.py index 0cb18b948..5f1c3605c 100644 --- a/llama_stack/models/llama/llama3/multimodal/model.py +++ b/llama_stack/models/llama/llama3/multimodal/model.py @@ -6,8 +6,9 @@ import logging import math +from collections.abc import Callable from functools import partial -from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from typing import Any import fairscale.nn.model_parallel.initialize as fs_init import torch @@ -104,9 +105,9 @@ class ColumnParallelConv2dPatch(torch.nn.Module): self, in_channels: int, out_channels: int, - kernel_size: Union[int, Tuple[int, int]], - stride: Union[int, Tuple[int, int]], - bias: Optional[bool] = False, + kernel_size: int | tuple[int, int], + stride: int | tuple[int, int], + bias: bool | None = False, ) -> None: super().__init__() if isinstance(kernel_size, int): @@ -390,13 +391,13 @@ class VisionEncoder(nn.Module): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool = True, - missing_keys: List[str] = None, - unexpected_keys: List[str] = None, - error_msgs: List[str] = None, + missing_keys: list[str] = None, + unexpected_keys: list[str] = None, + error_msgs: list[str] = None, return_state_dict: bool = False, ) -> None: orig_pos_embed = state_dict.get(prefix + "positional_embedding") @@ -641,7 +642,7 @@ class FeedForward(nn.Module): dim: int, hidden_dim: int, multiple_of: int, - ffn_dim_multiplier: Optional[float], + ffn_dim_multiplier: float | None, ): """ Initialize the FeedForward module. @@ -983,7 +984,7 @@ class CrossAttentionTransformerBlock(torch.nn.Module): self, x: torch.Tensor, xattn_mask: torch.Tensor, - full_text_row_masked_out_mask: Tuple[torch.Tensor, torch.Tensor], + full_text_row_masked_out_mask: tuple[torch.Tensor, torch.Tensor], xattn_cache: torch.Tensor, ) -> torch.Tensor: _attn_out = self.attention( @@ -1144,7 +1145,7 @@ class CrossAttentionTransformerText(torch.nn.Module): def _init_fusion_schedule( self, num_layers: int, - ) -> List[int]: + ) -> list[int]: llama_layers = list(range(self.n_llama_layers)) # uniformly spread the layers @@ -1231,7 +1232,7 @@ class CrossAttentionTransformerText(torch.nn.Module): text_dtype, vision_tokens, cross_attention_masks, - ) -> Tuple[Tensor, Tensor]: + ) -> tuple[Tensor, Tensor]: assert vision_tokens is not None, "Vision tokens must be provided" vision_seqlen = vision_tokens.shape[3] assert vision_tokens.shape[1] == cross_attention_masks.shape[2], ( @@ -1280,11 +1281,11 @@ class CrossAttentionTransformer(torch.nn.Module): def compute_vision_tokens_masks( self, - batch_images: List[List[PIL_Image.Image]], - batch_masks: List[List[List[int]]], + batch_images: list[list[PIL_Image.Image]], + batch_masks: list[list[list[int]]], total_len: int, device: torch.device, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: skip_vision_encoder = False assert len(batch_images) == len(batch_masks), "Images and masks must have the same length" @@ -1371,11 +1372,11 @@ class CrossAttentionTransformer(torch.nn.Module): def _stack_images( - images: List[List[PIL_Image.Image]], + images: list[list[PIL_Image.Image]], max_num_chunks: int, image_res: int, max_num_images: int, -) -> Tuple[torch.Tensor, List[int]]: +) -> tuple[torch.Tensor, list[int]]: """ Takes a list of list of images and stacks them into a tensor. This function is needed since images can be of completely @@ -1400,8 +1401,8 @@ def _stack_images( def _pad_masks( - all_masks: List[List[List[int]]], - all_num_chunks: List[List[int]], + all_masks: list[list[list[int]]], + all_num_chunks: list[list[int]], total_len: int, max_num_chunks: int, ) -> torch.Tensor: diff --git a/llama_stack/models/llama/llama3/prompt_templates/base.py b/llama_stack/models/llama/llama3/prompt_templates/base.py index bff2a21e1..0081443be 100644 --- a/llama_stack/models/llama/llama3/prompt_templates/base.py +++ b/llama_stack/models/llama/llama3/prompt_templates/base.py @@ -12,7 +12,7 @@ # the top-level of this source tree. from dataclasses import dataclass -from typing import Any, Dict, List +from typing import Any from jinja2 import Template @@ -20,7 +20,7 @@ from jinja2 import Template @dataclass class PromptTemplate: template: str - data: Dict[str, Any] + data: dict[str, Any] def render(self): template = Template(self.template) @@ -35,5 +35,5 @@ class PromptTemplateGeneratorBase: def gen(self, *args, **kwargs) -> PromptTemplate: raise NotImplementedError() - def data_examples(self) -> List[Any]: + def data_examples(self) -> list[Any]: raise NotImplementedError() diff --git a/llama_stack/models/llama/llama3/prompt_templates/system_prompts.py b/llama_stack/models/llama/llama3/prompt_templates/system_prompts.py index fbc0127fd..8e6f97012 100644 --- a/llama_stack/models/llama/llama3/prompt_templates/system_prompts.py +++ b/llama_stack/models/llama/llama3/prompt_templates/system_prompts.py @@ -13,7 +13,7 @@ import textwrap from datetime import datetime -from typing import Any, List, Optional +from typing import Any from llama_stack.apis.inference import ( BuiltinTool, @@ -39,12 +39,12 @@ class SystemDefaultGenerator(PromptTemplateGeneratorBase): }, ) - def data_examples(self) -> List[Any]: + def data_examples(self) -> list[Any]: return [None] class BuiltinToolGenerator(PromptTemplateGeneratorBase): - def _tool_breakdown(self, tools: List[ToolDefinition]): + def _tool_breakdown(self, tools: list[ToolDefinition]): builtin_tools, custom_tools = [], [] for dfn in tools: if isinstance(dfn.tool_name, BuiltinTool): @@ -54,7 +54,7 @@ class BuiltinToolGenerator(PromptTemplateGeneratorBase): return builtin_tools, custom_tools - def gen(self, tools: List[ToolDefinition]) -> PromptTemplate: + def gen(self, tools: list[ToolDefinition]) -> PromptTemplate: builtin_tools, custom_tools = self._tool_breakdown(tools) template_str = textwrap.dedent( """ @@ -75,7 +75,7 @@ class BuiltinToolGenerator(PromptTemplateGeneratorBase): }, ) - def data_examples(self) -> List[List[ToolDefinition]]: + def data_examples(self) -> list[list[ToolDefinition]]: return [ # builtin tools [ @@ -91,7 +91,7 @@ class BuiltinToolGenerator(PromptTemplateGeneratorBase): class JsonCustomToolGenerator(PromptTemplateGeneratorBase): - def gen(self, custom_tools: List[ToolDefinition]) -> PromptTemplate: + def gen(self, custom_tools: list[ToolDefinition]) -> PromptTemplate: template_str = textwrap.dedent( """ Answer the user's question by making use of the following functions if needed. @@ -137,7 +137,7 @@ class JsonCustomToolGenerator(PromptTemplateGeneratorBase): {"custom_tools": [t.model_dump() for t in custom_tools]}, ) - def data_examples(self) -> List[List[ToolDefinition]]: + def data_examples(self) -> list[list[ToolDefinition]]: return [ [ ToolDefinition( @@ -161,7 +161,7 @@ class JsonCustomToolGenerator(PromptTemplateGeneratorBase): class FunctionTagCustomToolGenerator(PromptTemplateGeneratorBase): - def gen(self, custom_tools: List[ToolDefinition]) -> PromptTemplate: + def gen(self, custom_tools: list[ToolDefinition]) -> PromptTemplate: template_str = textwrap.dedent( """ You have access to the following functions: @@ -199,7 +199,7 @@ class FunctionTagCustomToolGenerator(PromptTemplateGeneratorBase): {"custom_tools": [t.model_dump() for t in custom_tools]}, ) - def data_examples(self) -> List[List[ToolDefinition]]: + def data_examples(self) -> list[list[ToolDefinition]]: return [ [ ToolDefinition( @@ -238,14 +238,14 @@ class PythonListCustomToolGenerator(PromptTemplateGeneratorBase): # noqa: N801 """.strip("\n") ) - def gen(self, custom_tools: List[ToolDefinition], system_prompt: Optional[str] = None) -> PromptTemplate: + def gen(self, custom_tools: list[ToolDefinition], system_prompt: str | None = None) -> PromptTemplate: system_prompt = system_prompt or self.DEFAULT_PROMPT return PromptTemplate( system_prompt, {"function_description": self._gen_function_description(custom_tools)}, ) - def _gen_function_description(self, custom_tools: List[ToolDefinition]) -> PromptTemplate: + def _gen_function_description(self, custom_tools: list[ToolDefinition]) -> PromptTemplate: template_str = textwrap.dedent( """ Here is a list of functions in JSON format that you can invoke. @@ -291,7 +291,7 @@ class PythonListCustomToolGenerator(PromptTemplateGeneratorBase): # noqa: N801 {"tools": [t.model_dump() for t in custom_tools]}, ).render() - def data_examples(self) -> List[List[ToolDefinition]]: + def data_examples(self) -> list[list[ToolDefinition]]: return [ [ ToolDefinition( diff --git a/llama_stack/models/llama/llama3/prompt_templates/tool_response.py b/llama_stack/models/llama/llama3/prompt_templates/tool_response.py index 3df4dac14..4da171279 100644 --- a/llama_stack/models/llama/llama3/prompt_templates/tool_response.py +++ b/llama_stack/models/llama/llama3/prompt_templates/tool_response.py @@ -12,7 +12,6 @@ # the top-level of this source tree. import textwrap -from typing import Optional from .base import PromptTemplate, PromptTemplateGeneratorBase @@ -21,8 +20,8 @@ class ToolResponseGenerator(PromptTemplateGeneratorBase): def gen( self, status: str, - stdout: Optional[str] = None, - stderr: Optional[str] = None, + stdout: str | None = None, + stderr: str | None = None, ): assert status in [ "success", diff --git a/llama_stack/models/llama/llama3/quantization/loader.py b/llama_stack/models/llama/llama3/quantization/loader.py index 771fd02be..436cfa6fa 100644 --- a/llama_stack/models/llama/llama3/quantization/loader.py +++ b/llama_stack/models/llama/llama3/quantization/loader.py @@ -6,7 +6,7 @@ # type: ignore import os -from typing import Any, Dict, List, Optional, cast +from typing import Any, cast import torch from fairscale.nn.model_parallel.initialize import get_model_parallel_rank @@ -37,9 +37,9 @@ def swiglu_wrapper( def convert_to_quantized_model( model: Transformer | CrossAttentionTransformer, checkpoint_dir: str, - quantization_mode: Optional[str] = None, - fp8_activation_scale_ub: Optional[float] = 1200.0, - device: Optional[torch.device] = None, + quantization_mode: str | None = None, + fp8_activation_scale_ub: float | None = 1200.0, + device: torch.device | None = None, ) -> Transformer | CrossAttentionTransformer: if quantization_mode == QuantizationMode.fp8_mixed: return convert_to_fp8_quantized_model(model, checkpoint_dir, fp8_activation_scale_ub, device) @@ -52,8 +52,8 @@ def convert_to_quantized_model( def convert_to_fp8_quantized_model( model: Transformer, checkpoint_dir: str, - fp8_activation_scale_ub: Optional[float] = 1200.0, - device: Optional[torch.device] = None, + fp8_activation_scale_ub: float | None = 1200.0, + device: torch.device | None = None, ) -> Transformer: # Move weights to GPU with quantization fp8_scales_path = os.path.join(checkpoint_dir, f"fp8_scales_{get_model_parallel_rank()}.pt") @@ -122,8 +122,8 @@ class Int8DynActInt4WeightLinearLoRA(Int8DynActInt4WeightLinear): precision: torch.dtype = torch.float32, scales_precision: torch.dtype = torch.float32, # LoRA parameters - lora_rank: Optional[int] = None, - lora_scale: Optional[float] = None, + lora_rank: int | None = None, + lora_scale: float | None = None, ) -> None: super().__init__( in_features, @@ -134,8 +134,8 @@ class Int8DynActInt4WeightLinearLoRA(Int8DynActInt4WeightLinear): precision=precision, scales_precision=scales_precision, ) - self.lora_scale: Optional[float] = None - self.adaptor: Optional[nn.Sequential] = None + self.lora_scale: float | None = None + self.adaptor: nn.Sequential | None = None if lora_rank is not None: assert lora_scale is not None, "Please specify lora scale for LoRA." # Low-rank adaptation. See paper for more details: https://arxiv.org/abs/2106.09685 @@ -147,13 +147,13 @@ class Int8DynActInt4WeightLinearLoRA(Int8DynActInt4WeightLinear): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool, - missing_keys: List[str], - unexpected_keys: List[str], - error_msgs: List[str], + missing_keys: list[str], + unexpected_keys: list[str], + error_msgs: list[str], ) -> None: """A hook to load the quantized weights from the state dict.""" if prefix + "zeros" not in state_dict: @@ -191,13 +191,13 @@ class Int8WeightEmbedding(torch.nn.Embedding): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool, - missing_keys: List[str], - unexpected_keys: List[str], - error_msgs: List[str], + missing_keys: list[str], + unexpected_keys: list[str], + error_msgs: list[str], ) -> None: """A hook to load the quantized embedding weight and scales from the state dict.""" weights = state_dict.pop(prefix + "weight") @@ -221,13 +221,13 @@ class Int8WeightLinear(torch.nn.Linear): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool, - missing_keys: List[str], - unexpected_keys: List[str], - error_msgs: List[str], + missing_keys: list[str], + unexpected_keys: list[str], + error_msgs: list[str], ) -> None: """A hook to load the quantized linear weight and scales from the state dict.""" weights = state_dict.pop(prefix + "weight") @@ -238,8 +238,8 @@ class Int8WeightLinear(torch.nn.Linear): def _prepare_model_int4_weight_int8_dynamic_activation( model: torch.nn.Module, group_size: int, - lora_rank: Optional[int], - lora_scale: Optional[float], + lora_rank: int | None, + lora_scale: float | None, ): """Prepare the model for int4 weight and int8 dynamic activation quantization. @@ -265,7 +265,7 @@ def _prepare_model_int4_weight_int8_dynamic_activation( ) del module setattr(model, module_name, quantized_module) - elif isinstance(module, (ColumnParallelLinear, RowParallelLinear, nn.Linear)): + elif isinstance(module, ColumnParallelLinear | RowParallelLinear | nn.Linear): quantized_module = Int8DynActInt4WeightLinearLoRA( in_features=module.in_features, out_features=module.out_features, @@ -286,7 +286,7 @@ def _prepare_model_int4_weight_int8_dynamic_activation( def convert_to_int4_quantized_model( model: Transformer | CrossAttentionTransformer, checkpoint_dir: str, - device: Optional[torch.device] = None, + device: torch.device | None = None, ) -> Transformer | CrossAttentionTransformer: """Convert the model to int4 quantized model.""" model_args = model.params diff --git a/llama_stack/models/llama/llama3/tokenizer.py b/llama_stack/models/llama/llama3/tokenizer.py index d3cc4fc07..e5ada3599 100644 --- a/llama_stack/models/llama/llama3/tokenizer.py +++ b/llama_stack/models/llama/llama3/tokenizer.py @@ -5,18 +5,11 @@ # the root directory of this source tree. import os +from collections.abc import Collection, Iterator, Sequence, Set from logging import getLogger from pathlib import Path from typing import ( - AbstractSet, - Collection, - Dict, - Iterator, - List, Literal, - Optional, - Sequence, - Union, cast, ) @@ -44,7 +37,7 @@ class Tokenizer: Tokenizing and encoding/decoding text using the Tiktoken tokenizer. """ - special_tokens: Dict[str, int] + special_tokens: dict[str, int] num_reserved_special_tokens = 256 @@ -116,9 +109,9 @@ class Tokenizer: *, bos: bool, eos: bool, - allowed_special: Optional[Union[Literal["all"], AbstractSet[str]]] = None, - disallowed_special: Union[Literal["all"], Collection[str]] = (), - ) -> List[int]: + allowed_special: Literal["all"] | Set[str] | None = None, + disallowed_special: Literal["all"] | Collection[str] = (), + ) -> list[int]: """ Encodes a string into a list of token IDs. @@ -151,7 +144,7 @@ class Tokenizer: s[i : i + TIKTOKEN_MAX_ENCODE_CHARS], MAX_NO_WHITESPACES_CHARS ) ) - t: List[int] = [] + t: list[int] = [] for substr in substrs: t.extend( self.model.encode( @@ -177,7 +170,7 @@ class Tokenizer: str: The decoded string. """ # Typecast is safe here. Tiktoken doesn't do anything list-related with the sequence. - return self.model.decode(cast(List[int], t)) + return self.model.decode(cast(list[int], t)) @staticmethod def _split_whitespaces_or_nonwhitespaces(s: str, max_consecutive_slice_len: int) -> Iterator[str]: diff --git a/llama_stack/models/llama/llama3/tool_utils.py b/llama_stack/models/llama/llama3/tool_utils.py index 91b46ec98..574080184 100644 --- a/llama_stack/models/llama/llama3/tool_utils.py +++ b/llama_stack/models/llama/llama3/tool_utils.py @@ -6,7 +6,6 @@ import json import re -from typing import Optional, Tuple from llama_stack.log import get_logger @@ -172,7 +171,7 @@ class ToolUtils: return match is not None @staticmethod - def maybe_extract_builtin_tool_call(message_body: str) -> Optional[Tuple[str, str]]: + def maybe_extract_builtin_tool_call(message_body: str) -> tuple[str, str] | None: # Find the first match in the text match = re.search(BUILTIN_TOOL_PATTERN, message_body) @@ -185,7 +184,7 @@ class ToolUtils: return None @staticmethod - def maybe_extract_custom_tool_call(message_body: str) -> Optional[Tuple[str, str]]: + def maybe_extract_custom_tool_call(message_body: str) -> tuple[str, str] | None: # NOTE: Custom function too calls are still experimental # Sometimes, response is of the form # {"type": "function", "name": "function_name", "parameters": {...} @@ -252,7 +251,7 @@ class ToolUtils: def format_value(value: RecursiveType) -> str: if isinstance(value, str): return f'"{value}"' - elif isinstance(value, (int, float, bool)) or value is None: + elif isinstance(value, int | float | bool) or value is None: return str(value) elif isinstance(value, list): return f"[{', '.join(format_value(v) for v in value)}]" diff --git a/llama_stack/models/llama/llama3_1/prompts.py b/llama_stack/models/llama/llama3_1/prompts.py index 9dcc51dc8..579a5ee02 100644 --- a/llama_stack/models/llama/llama3_1/prompts.py +++ b/llama_stack/models/llama/llama3_1/prompts.py @@ -12,7 +12,6 @@ # the top-level of this source tree. import textwrap -from typing import List from llama_stack.models.llama.datatypes import ( BuiltinTool, @@ -73,7 +72,7 @@ def wolfram_alpha_response(): ) -def usecases() -> List[UseCase | str]: +def usecases() -> list[UseCase | str]: return [ textwrap.dedent( """ diff --git a/llama_stack/models/llama/llama3_3/prompts.py b/llama_stack/models/llama/llama3_3/prompts.py index 194e4fa26..60349e578 100644 --- a/llama_stack/models/llama/llama3_3/prompts.py +++ b/llama_stack/models/llama/llama3_3/prompts.py @@ -12,7 +12,6 @@ # the top-level of this source tree. import textwrap -from typing import List from llama_stack.models.llama.datatypes import ( BuiltinTool, @@ -74,7 +73,7 @@ def wolfram_alpha_response(): ) -def usecases() -> List[UseCase | str]: +def usecases() -> list[UseCase | str]: return [ textwrap.dedent( """ diff --git a/llama_stack/models/llama/llama4/args.py b/llama_stack/models/llama/llama4/args.py index dd5f7cbde..523d6ed10 100644 --- a/llama_stack/models/llama/llama4/args.py +++ b/llama_stack/models/llama/llama4/args.py @@ -5,7 +5,6 @@ # the root directory of this source tree. from enum import Enum -from typing import Optional from pydantic import BaseModel, model_validator @@ -15,8 +14,8 @@ class QuantizationScheme(Enum): class QuantizationArgs(BaseModel): - scheme: Optional[QuantizationScheme] = None - group_size: Optional[int] = None + scheme: QuantizationScheme | None = None + group_size: int | None = None spinquant: bool = False @@ -58,32 +57,32 @@ class ModelArgs(BaseModel): dim: int = -1 n_layers: int = -1 n_heads: int = -1 - n_kv_heads: Optional[int] = None - head_dim: Optional[int] = None + n_kv_heads: int | None = None + head_dim: int | None = None vocab_size: int = -1 multiple_of: int = 256 # make SwiGLU hidden layer size multiple of large power of 2 - ffn_dim_multiplier: Optional[float] = None - ffn_exp: Optional[float] = None + ffn_dim_multiplier: float | None = None + ffn_exp: float | None = None norm_eps: float = 1e-5 - attention_chunk_size: Optional[int] = None + attention_chunk_size: int | None = None rope_theta: float = 500000 use_scaled_rope: bool = False - rope_scaling_factor: Optional[float] = None - rope_high_freq_factor: Optional[float] = None + rope_scaling_factor: float | None = None + rope_high_freq_factor: float | None = None - nope_layer_interval: Optional[int] = None # No position encoding in every n layers + nope_layer_interval: int | None = None # No position encoding in every n layers use_qk_norm: bool = False # Set to True to enable inference-time temperature tuning (useful for very long context) attn_temperature_tuning: bool = False floor_scale: float = 8192.0 attn_scale: float = 0.1 - vision_args: Optional[VisionArgs] = None - moe_args: Optional[MoEArgs] = None - quantization_args: Optional[QuantizationArgs] = None - lora_args: Optional[LoRAArgs] = None + vision_args: VisionArgs | None = None + moe_args: MoEArgs | None = None + quantization_args: QuantizationArgs | None = None + lora_args: LoRAArgs | None = None max_batch_size: int = 32 max_seq_len: int = 2048 diff --git a/llama_stack/models/llama/llama4/chat_format.py b/llama_stack/models/llama/llama4/chat_format.py index 9d60d00e9..96ebd0881 100644 --- a/llama_stack/models/llama/llama4/chat_format.py +++ b/llama_stack/models/llama/llama4/chat_format.py @@ -5,9 +5,9 @@ # the root directory of this source tree. import io +import json import uuid from dataclasses import dataclass -from typing import Dict, List, Optional, Tuple import torch from PIL import Image as PIL_Image @@ -45,10 +45,10 @@ def role_str(role: Role) -> str: class TransformedImage: image_tiles: torch.Tensor # is the aspect ratio needed anywhere? - aspect_ratio: Tuple[int, int] + aspect_ratio: tuple[int, int] -def convert_image_to_rgb(image: PIL_Image.Image, bg: Tuple[int, int, int] = (255, 255, 255)) -> PIL_Image.Image: +def convert_image_to_rgb(image: PIL_Image.Image, bg: tuple[int, int, int] = (255, 255, 255)) -> PIL_Image.Image: if image.mode == "RGBA": image.load() # for png.split() new_img = PIL_Image.new("RGB", image.size, bg) @@ -58,12 +58,12 @@ def convert_image_to_rgb(image: PIL_Image.Image, bg: Tuple[int, int, int] = (255 class ChatFormat: - possible_headers: Dict[Role, str] + possible_headers: dict[Role, str] def __init__( self, tokenizer: Tokenizer, - vision_args: Optional[VisionArgs] = None, + vision_args: VisionArgs | None = None, max_num_chunks: int = 16, ): self.tokenizer = tokenizer @@ -80,7 +80,7 @@ class ChatFormat: vision_args.image_size.width, vision_args.image_size.height ) - def _encode_header(self, role: str) -> List[int]: + def _encode_header(self, role: str) -> list[int]: tokens = [] tokens.append(self.tokenizer.special_tokens["<|header_start|>"]) @@ -97,7 +97,7 @@ class ChatFormat: def _encode_image( self, transformed_image: TransformedImage, - ) -> List[int]: + ) -> list[int]: assert self.vision_args is not None, "The model is not vision-enabled" image_tensor = transformed_image.image_tiles @@ -139,7 +139,7 @@ class ChatFormat: return tokens - def _encode_content(self, content: RawContent, bos: bool = False) -> Tuple[List[int], List[TransformedImage]]: + def _encode_content(self, content: RawContent, bos: bool = False) -> tuple[list[int], list[TransformedImage]]: tokens = [] tranformed_images = [] @@ -188,7 +188,7 @@ class ChatFormat: def encode_message( self, message: RawMessage, tool_prompt_format: ToolPromptFormat - ) -> Tuple[List[int], List[TransformedImage]]: + ) -> tuple[list[int], list[TransformedImage]]: tokens = self._encode_header(message.role) images = [] @@ -222,7 +222,7 @@ class ChatFormat: def encode_dialog_prompt( self, - messages: List[RawMessage], + messages: list[RawMessage], tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json, ) -> LLMInput: tokens = [] @@ -239,7 +239,7 @@ class ChatFormat: return self._model_input_from_tokens_images(tokens, images) # TODO(this should be generic, not only for assistant messages) - def decode_assistant_message(self, tokens: List[int], stop_reason: StopReason) -> RawMessage: + def decode_assistant_message(self, tokens: list[int], stop_reason: StopReason) -> RawMessage: content = self.tokenizer.decode(tokens) return self.decode_assistant_message_from_content(content, stop_reason) @@ -299,8 +299,10 @@ class ChatFormat: call_id=call_id, tool_name=tool_name, arguments=tool_arguments, + arguments_json=json.dumps(tool_arguments), ) ) + content = "" return RawMessage( role="assistant", @@ -309,7 +311,7 @@ class ChatFormat: tool_calls=tool_calls, ) - def _model_input_from_tokens_images(self, tokens: List[int], images: List[TransformedImage]) -> LLMInput: + def _model_input_from_tokens_images(self, tokens: list[int], images: list[TransformedImage]) -> LLMInput: return LLMInput( tokens=tokens, images=[x.image_tiles for x in images] if len(images) > 0 else None, diff --git a/llama_stack/models/llama/llama4/datatypes.py b/llama_stack/models/llama/llama4/datatypes.py index 27174db63..24d8ae948 100644 --- a/llama_stack/models/llama/llama4/datatypes.py +++ b/llama_stack/models/llama/llama4/datatypes.py @@ -5,7 +5,6 @@ # the root directory of this source tree. from dataclasses import dataclass -from typing import List, Optional, Union import torch @@ -30,7 +29,7 @@ class LLMInput: tokens: torch.Tensor # images are already pre-processed (resized, tiled, etc.) - images: Optional[List[torch.Tensor]] = None + images: list[torch.Tensor] | None = None @dataclass @@ -45,8 +44,8 @@ class TransformerInput: # tokens_position defines the position of the tokens in each batch, # - when it is a tensor ([batch_size,]), it is the start position of the tokens in each batch # - when it is an int, the start position are the same for all batches - tokens_position: Union[torch.Tensor, int] - image_embedding: Optional[MaskedEmbedding] = None + tokens_position: torch.Tensor | int + image_embedding: MaskedEmbedding | None = None @dataclass diff --git a/llama_stack/models/llama/llama4/ffn.py b/llama_stack/models/llama/llama4/ffn.py index 9c9fca5fc..6584f1a2a 100644 --- a/llama_stack/models/llama/llama4/ffn.py +++ b/llama_stack/models/llama/llama4/ffn.py @@ -11,7 +11,7 @@ # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. -from typing import Any, Dict, List +from typing import Any from fairscale.nn.model_parallel.layers import ColumnParallelLinear, RowParallelLinear from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region @@ -36,13 +36,13 @@ class FeedForward(nn.Module): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool, - missing_keys: List[str], - unexpected_keys: List[str], - error_msgs: List[str], + missing_keys: list[str], + unexpected_keys: list[str], + error_msgs: list[str], ) -> None: if prefix + "mlp.fc1_weight" in state_dict: w1, w3 = state_dict.pop(prefix + "mlp.fc1_weight").chunk(2, dim=0) diff --git a/llama_stack/models/llama/llama4/generation.py b/llama_stack/models/llama/llama4/generation.py index 8e94bb33a..476761209 100644 --- a/llama_stack/models/llama/llama4/generation.py +++ b/llama_stack/models/llama/llama4/generation.py @@ -10,8 +10,8 @@ import json import os import sys import time +from collections.abc import Callable, Generator from pathlib import Path -from typing import Callable, Generator, List, Optional import torch import torch.nn.functional as F @@ -38,8 +38,8 @@ class Llama4: ckpt_dir: str, max_seq_len: int, max_batch_size: int, - world_size: Optional[int] = None, - quantization_mode: Optional[QuantizationMode] = None, + world_size: int | None = None, + quantization_mode: QuantizationMode | None = None, seed: int = 1, ): if not torch.distributed.is_initialized(): @@ -63,7 +63,7 @@ class Llama4: ckpt_paths = sorted(Path(ckpt_dir).glob("*.pth")) assert len(ckpt_paths) > 0, f"no checkpoint files found in {ckpt_dir}" print(f"Loading a checkpoint (shards={len(ckpt_paths)}, current-mp-size={world_size})") - with open(Path(ckpt_dir) / "params.json", "r") as f: + with open(Path(ckpt_dir) / "params.json") as f: params = json.loads(f.read()) model_args: ModelArgs = ModelArgs( @@ -117,15 +117,15 @@ class Llama4: @torch.inference_mode() def generate( self, - llm_inputs: List[LLMInput], + llm_inputs: list[LLMInput], temperature: float = 0.6, top_p: float = 0.9, - max_gen_len: Optional[int] = None, + max_gen_len: int | None = None, logprobs: bool = False, echo: bool = False, print_model_input: bool = False, - logits_processor: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, - ) -> Generator[List[GenerationResult], None, None]: + logits_processor: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = None, + ) -> Generator[list[GenerationResult], None, None]: if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.model.args.max_seq_len: max_gen_len = self.model.args.max_seq_len - 1 @@ -245,13 +245,13 @@ class Llama4: def completion( self, - contents: List[RawContent], + contents: list[RawContent], temperature: float = 0.6, top_p: float = 0.9, - max_gen_len: Optional[int] = None, + max_gen_len: int | None = None, logprobs: bool = False, echo: bool = False, - ) -> Generator[List[GenerationResult], None, None]: + ) -> Generator[list[GenerationResult], None, None]: llm_inputs = [self.formatter.encode_content(c) for c in contents] for result in self.generate( llm_inputs=llm_inputs, @@ -267,13 +267,13 @@ class Llama4: def chat_completion( self, - messages_batch: List[List[RawMessage]], + messages_batch: list[list[RawMessage]], temperature: float = 0.6, top_p: float = 0.9, - max_gen_len: Optional[int] = None, + max_gen_len: int | None = None, logprobs: bool = False, echo: bool = False, - ) -> Generator[List[GenerationResult], None, None]: + ) -> Generator[list[GenerationResult], None, None]: llm_inputs = [self.formatter.encode_dialog_prompt(messages) for messages in messages_batch] for result in self.generate( llm_inputs=llm_inputs, diff --git a/llama_stack/models/llama/llama4/model.py b/llama_stack/models/llama/llama4/model.py index 2272b868d..4fb1181f7 100644 --- a/llama_stack/models/llama/llama4/model.py +++ b/llama_stack/models/llama/llama4/model.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import math -from typing import Any, Dict, List, Optional, Tuple +from typing import Any import fairscale.nn.model_parallel.initialize as fs_init import torch @@ -89,7 +89,7 @@ def apply_rotary_emb( xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor, -) -> Tuple[torch.Tensor, torch.Tensor]: +) -> tuple[torch.Tensor, torch.Tensor]: xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) freqs_cis = reshape_for_broadcast(freqs_cis, xq_) @@ -174,13 +174,13 @@ class Attention(nn.Module): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool, - missing_keys: List[str], - unexpected_keys: List[str], - error_msgs: List[str], + missing_keys: list[str], + unexpected_keys: list[str], + error_msgs: list[str], ) -> None: if prefix + "wqkv.weight" in state_dict: wqkv = state_dict.pop(prefix + "wqkv.weight") @@ -200,7 +200,7 @@ class Attention(nn.Module): x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, - mask: Optional[torch.Tensor] = None, + mask: torch.Tensor | None = None, ): bsz, seqlen, _ = x.shape xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) @@ -288,13 +288,13 @@ class TransformerBlock(nn.Module): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool, - missing_keys: List[str], - unexpected_keys: List[str], - error_msgs: List[str], + missing_keys: list[str], + unexpected_keys: list[str], + error_msgs: list[str], ) -> None: if prefix + "attention.wqkv.layer_norm_weight" in state_dict: state_dict[prefix + "attention_norm.weight"] = state_dict.pop(prefix + "attention.wqkv.layer_norm_weight") @@ -318,8 +318,8 @@ class TransformerBlock(nn.Module): x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, - global_attn_mask: Optional[torch.Tensor], - local_attn_mask: Optional[torch.Tensor], + global_attn_mask: torch.Tensor | None, + local_attn_mask: torch.Tensor | None, ): # The iRoPE architecture uses global attention mask for NoPE layers or # if chunked local attention is not used @@ -374,13 +374,13 @@ class Transformer(nn.Module): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool, - missing_keys: List[str], - unexpected_keys: List[str], - error_msgs: List[str], + missing_keys: list[str], + unexpected_keys: list[str], + error_msgs: list[str], ) -> None: if prefix + "rope.freqs" in state_dict: state_dict.pop(prefix + "rope.freqs") diff --git a/llama_stack/models/llama/llama4/moe.py b/llama_stack/models/llama/llama4/moe.py index 2ce49e915..7475963d3 100644 --- a/llama_stack/models/llama/llama4/moe.py +++ b/llama_stack/models/llama/llama4/moe.py @@ -6,7 +6,7 @@ # ruff: noqa: N806 # pyre-strict -from typing import Any, Dict, List +from typing import Any import fairscale.nn.model_parallel.initialize as fs_init import torch @@ -63,13 +63,13 @@ class Experts(nn.Module): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool, - missing_keys: List[str], - unexpected_keys: List[str], - error_msgs: List[str], + missing_keys: list[str], + unexpected_keys: list[str], + error_msgs: list[str], ) -> None: self.prefix = prefix if prefix + "moe_w_in_eD_F" in state_dict: @@ -158,13 +158,13 @@ class MoE(torch.nn.Module): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool, - missing_keys: List[str], - unexpected_keys: List[str], - error_msgs: List[str], + missing_keys: list[str], + unexpected_keys: list[str], + error_msgs: list[str], ) -> None: if prefix + "w_in_shared_FD.weight" in state_dict: state_dict[prefix + "shared_expert.w1.weight"] = state_dict.pop(prefix + "w_in_shared_FD.weight") @@ -210,5 +210,5 @@ class MoE(torch.nn.Module): def divide_exact(numerator: int, denominator: int) -> int: - assert numerator % denominator == 0, "{} is not divisible by {}".format(numerator, denominator) + assert numerator % denominator == 0, f"{numerator} is not divisible by {denominator}" return numerator // denominator diff --git a/llama_stack/models/llama/llama4/preprocess.py b/llama_stack/models/llama/llama4/preprocess.py index 689680779..7527a9987 100644 --- a/llama_stack/models/llama/llama4/preprocess.py +++ b/llama_stack/models/llama/llama4/preprocess.py @@ -13,7 +13,6 @@ import math from collections import defaultdict -from typing import Optional, Set, Tuple import torch import torchvision.transforms as tv @@ -52,7 +51,7 @@ class ResizeNormalizeImageTransform: return self.tv_transform(image) -class VariableSizeImageTransform(object): +class VariableSizeImageTransform: """ This class accepts images of any size and dynamically resize, pads and chunks it based on the image aspect ratio and the number of image chunks we allow. @@ -100,7 +99,7 @@ class VariableSizeImageTransform(object): self.resample = tv.InterpolationMode.BILINEAR @staticmethod - def get_factors(n: int) -> Set[int]: + def get_factors(n: int) -> set[int]: """ Calculate all factors of a given number, i.e. a dividor that leaves no remainder. For example, if n=12, it will return {1, 2, 3, 4, 6, 12}. @@ -170,9 +169,9 @@ class VariableSizeImageTransform(object): @staticmethod def get_max_res_without_distortion( - image_size: Tuple[int, int], - target_size: Tuple[int, int], - ) -> Tuple[int, int]: + image_size: tuple[int, int], + target_size: tuple[int, int], + ) -> tuple[int, int]: """ Determines the maximum resolution to which an image can be resized to without distorting its aspect ratio, based on the target resolution. @@ -223,8 +222,8 @@ class VariableSizeImageTransform(object): def resize_without_distortion( self, image: torch.Tensor, - target_size: Tuple[int, int], - max_upscaling_size: Optional[int], + target_size: tuple[int, int], + max_upscaling_size: int | None, ) -> torch.Tensor: """ Used to resize an image to target_resolution, without distortion. @@ -289,10 +288,10 @@ class VariableSizeImageTransform(object): def get_best_fit( self, - image_size: Tuple[int, int], + image_size: tuple[int, int], possible_resolutions: torch.Tensor, resize_to_max_canvas: bool = False, - ) -> Tuple[int, int]: + ) -> tuple[int, int]: """ Determines the best canvas possible from a list of possible resolutions to, without distortion, resize an image to. @@ -392,7 +391,7 @@ class VariableSizeImageTransform(object): max_num_chunks: int, normalize_img: bool = True, resize_to_max_canvas: bool = False, - ) -> Tuple[torch.Tensor, Tuple[int, int]]: + ) -> tuple[torch.Tensor, tuple[int, int]]: """ Args: image (PIL.Image): Image to be resized. diff --git a/llama_stack/models/llama/llama4/prompt_format.md b/llama_stack/models/llama/llama4/prompt_format.md index 698571093..350a5517a 100644 --- a/llama_stack/models/llama/llama4/prompt_format.md +++ b/llama_stack/models/llama/llama4/prompt_format.md @@ -64,7 +64,7 @@ This example passes an image that is smaller than the tile size, to show the til ##### Model Response Format ``` -The image depicts a dog standing on a skateboard, with its front paws positioned on the board and its back paws hanging off the back. The dog has a distinctive coat pattern, featuring a white face, brown and black fur, and white paws, and is standing on a skateboard with red wheels, set against a blurred background of a street or alleyway with a teal door and beige wall.<|eot|> +The image depicts a dog standing on a skateboard, positioned centrally and facing the camera directly. The dog has a distinctive coat pattern featuring white, black, and brown fur, with floppy ears and a black nose, and is standing on a skateboard with red wheels.<|eot|> ``` @@ -91,7 +91,7 @@ Here is an example of how to pass an image to the model ##### Model Response Format ``` -This image shows a dog standing on a skateboard, with its front paws positioned near the front of the board and its back paws near the back. The dog has a white, black, and orange coat, and is standing on a gray skateboard with red wheels, in front of a blurred background that appears to be a street or alleyway.<|eot|> +The image depicts a dog standing on a skateboard, with the dog positioned centrally and facing forward. The dog has a distinctive coat featuring a mix of white, brown, and black fur, and is wearing a collar as it stands on the skateboard, which has red wheels.<|eot|> ``` @@ -117,7 +117,7 @@ Here is an example of how to pass an image to the model ##### Model Response Format ``` -The first image shows a dog standing on a skateboard, while the second image shows a plate of spaghetti with tomato sauce, parmesan cheese, and parsley. The two images are unrelated, with the first image featuring a dog and the second image featuring a food dish, and they do not share any common elements or themes.<|eot|> +The first image features a dog standing on a skateboard, while the second image showcases a plate of spaghetti with tomato sauce and cheese. The two images appear to be unrelated, with one depicting a playful scene of a dog on a skateboard and the other presenting a classic Italian dish.<|eom|> ``` @@ -135,13 +135,44 @@ We are continuing the format for zero shot function calling used in previous ver ``` <|begin_of_text|><|header_start|>system<|header_end|> -You are an expert in composing functions. You are given a question and a set of possible functions. -Based on the question, you will need to make one or more function/tool calls to achieve the purpose. -If none of the function can be used, point it out. If the given question lacks the parameters required by the function, -also point it out. You should only return the function call in tools call sections. +You are a helpful assistant and an expert in function composition. You can answer general questions using your internal knowledge OR invoke functions when necessary. Follow these strict guidelines: + +1. FUNCTION CALLS: +- ONLY use functions that are EXPLICITLY listed in the function list below +- If NO functions are listed (empty function list []), respond ONLY with internal knowledge or "I don't have access to [Unavailable service] information" +- If a function is not in the list, respond ONLY with internal knowledge or "I don't have access to [Unavailable service] information" +- If ALL required parameters are present AND the query EXACTLY matches a listed function's purpose: output ONLY the function call(s) +- Use exact format: [func_name1(param1=value1, param2=value2), func_name2(...)] +Examples: +CORRECT: [get_weather(location="Vancouver"), calculate_route(start="Boston", end="New York")] <- Only if get_weather and calculate_route are in function list +INCORRECT: get_weather(location="New York") +INCORRECT: Let me check the weather: [get_weather(location="New York")] +INCORRECT: [get_events(location="Singapore")] <- If function not in list + +2. RESPONSE RULES: +- For pure function requests matching a listed function: ONLY output the function call(s) +- For knowledge questions: ONLY output text +- For missing parameters: ONLY request the specific missing parameters +- For unavailable services (not in function list): output ONLY with internal knowledge or "I don't have access to [Unavailable service] information". Do NOT execute a function call. +- If the query asks for information beyond what a listed function provides: output ONLY with internal knowledge about your limitations +- NEVER combine text and function calls in the same response +- NEVER suggest alternative functions when the requested service is unavailable +- NEVER create or invent new functions not listed below + +3. STRICT BOUNDARIES: +- ONLY use functions from the list below - no exceptions +- NEVER use a function as an alternative to unavailable information +- NEVER call functions not present in the function list +- NEVER add explanatory text to function calls +- NEVER respond with empty brackets +- Use proper Python/JSON syntax for function calls +- Check the function list carefully before responding + +4. TOOL RESPONSE HANDLING: +- When receiving tool responses: provide concise, natural language responses +- Don't repeat tool response verbatim +- Don't add supplementary information -If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)] -You SHOULD NOT include any other text in the response. Here is a list of functions in JSON format that you can invoke. @@ -151,9 +182,7 @@ Here is a list of functions in JSON format that you can invoke. "description": "Get weather info for places", "parameters": { "type": "dict", - "required": [ - "city" - ], + "required": ["city"], "properties": { "city": { "type": "string", @@ -167,7 +196,10 @@ Here is a list of functions in JSON format that you can invoke. } } } -<|eot|><|header_start|>user<|header_end|> +] + +You can answer general questions or invoke tools when necessary. +In addition to tool calls, you should also augment your responses by using the tool outputs.<|eot|><|header_start|>user<|header_end|> What is the weather in SF and Seattle?<|eot|><|header_start|>assistant<|header_end|> @@ -176,7 +208,7 @@ What is the weather in SF and Seattle?<|eot|><|header_start|>assistant<|header_e ##### Model Response Format ``` -[get_weather(city='SF'), get_weather(city='Seattle')]<|eot|> +[get_weather(city="San Francisco"), get_weather(city="Seattle")]<|eot|> ``` @@ -273,5 +305,5 @@ Use tools to get latest trending songs<|eot|><|header_start|>assistant<|header_e ##### Model Response Format ``` -{"n": "10"}<|eot|> +{"n": 10}<|eot|> ``` diff --git a/llama_stack/models/llama/llama4/prompt_templates/system_prompts.py b/llama_stack/models/llama/llama4/prompt_templates/system_prompts.py new file mode 100644 index 000000000..ceb28300f --- /dev/null +++ b/llama_stack/models/llama/llama4/prompt_templates/system_prompts.py @@ -0,0 +1,143 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# top-level folder for each specific model found within the models/ directory at +# the top-level of this source tree. + +import textwrap + +from llama_stack.apis.inference import ToolDefinition, ToolParamDefinition +from llama_stack.models.llama.llama3.prompt_templates.base import ( + PromptTemplate, + PromptTemplateGeneratorBase, +) + + +class PythonListCustomToolGenerator(PromptTemplateGeneratorBase): # noqa: N801 + DEFAULT_PROMPT = textwrap.dedent( + """ + You are a helpful assistant and an expert in function composition. You can answer general questions using your internal knowledge OR invoke functions when necessary. Follow these strict guidelines: + + 1. FUNCTION CALLS: + - ONLY use functions that are EXPLICITLY listed in the function list below + - If NO functions are listed (empty function list []), respond ONLY with internal knowledge or "I don't have access to [Unavailable service] information" + - If a function is not in the list, respond ONLY with internal knowledge or "I don't have access to [Unavailable service] information" + - If ALL required parameters are present AND the query EXACTLY matches a listed function's purpose: output ONLY the function call(s) + - Use exact format: [func_name1(param1=value1, param2=value2), func_name2(...)] + Examples: + CORRECT: [get_weather(location="Vancouver"), calculate_route(start="Boston", end="New York")] <- Only if get_weather and calculate_route are in function list + INCORRECT: get_weather(location="New York") + INCORRECT: Let me check the weather: [get_weather(location="New York")] + INCORRECT: [get_events(location="Singapore")] <- If function not in list + + 2. RESPONSE RULES: + - For pure function requests matching a listed function: ONLY output the function call(s) + - For knowledge questions: ONLY output text + - For missing parameters: ONLY request the specific missing parameters + - For unavailable services (not in function list): output ONLY with internal knowledge or "I don't have access to [Unavailable service] information". Do NOT execute a function call. + - If the query asks for information beyond what a listed function provides: output ONLY with internal knowledge about your limitations + - NEVER combine text and function calls in the same response + - NEVER suggest alternative functions when the requested service is unavailable + - NEVER create or invent new functions not listed below + + 3. STRICT BOUNDARIES: + - ONLY use functions from the list below - no exceptions + - NEVER use a function as an alternative to unavailable information + - NEVER call functions not present in the function list + - NEVER add explanatory text to function calls + - NEVER respond with empty brackets + - Use proper Python/JSON syntax for function calls + - Check the function list carefully before responding + + 4. TOOL RESPONSE HANDLING: + - When receiving tool responses: provide concise, natural language responses + - Don't repeat tool response verbatim + - Don't add supplementary information + + + {{ function_description }} + """.strip("\n") + ) + + def gen(self, custom_tools: list[ToolDefinition], system_prompt: str | None = None) -> PromptTemplate: + system_prompt = system_prompt or self.DEFAULT_PROMPT + return PromptTemplate( + system_prompt, + {"function_description": self._gen_function_description(custom_tools)}, + ) + + def _gen_function_description(self, custom_tools: list[ToolDefinition]) -> PromptTemplate: + template_str = textwrap.dedent( + """ + Here is a list of functions in JSON format that you can invoke. + + [ + {% for t in tools -%} + {# manually setting up JSON because jinja sorts keys in unexpected ways -#} + {%- set tname = t.tool_name -%} + {%- set tdesc = t.description -%} + {%- set tparams = t.parameters -%} + {%- set required_params = [] -%} + {%- for name, param in tparams.items() if param.required == true -%} + {%- set _ = required_params.append(name) -%} + {%- endfor -%} + { + "name": "{{tname}}", + "description": "{{tdesc}}", + "parameters": { + "type": "dict", + "required": {{ required_params | tojson }}, + "properties": { + {%- for name, param in tparams.items() %} + "{{name}}": { + "type": "{{param.param_type}}", + "description": "{{param.description}}"{% if param.default %}, + "default": "{{param.default}}"{% endif %} + }{% if not loop.last %},{% endif %} + {%- endfor %} + } + } + }{% if not loop.last %}, + {% endif -%} + {%- endfor %} + ] + + You can answer general questions or invoke tools when necessary. + In addition to tool calls, you should also augment your responses by using the tool outputs. + + """ + ) + return PromptTemplate( + template_str.strip("\n"), + {"tools": [t.model_dump() for t in custom_tools]}, + ).render() + + def data_examples(self) -> list[list[ToolDefinition]]: + return [ + [ + ToolDefinition( + tool_name="get_weather", + description="Get weather info for places", + parameters={ + "city": ToolParamDefinition( + param_type="string", + description="The name of the city to get the weather for", + required=True, + ), + "metric": ToolParamDefinition( + param_type="string", + description="The metric for weather. Options are: celsius, fahrenheit", + required=False, + default="celsius", + ), + }, + ), + ] + ] diff --git a/llama_stack/models/llama/llama4/prompts.py b/llama_stack/models/llama/llama4/prompts.py index 13b96359a..2da94db7b 100644 --- a/llama_stack/models/llama/llama4/prompts.py +++ b/llama_stack/models/llama/llama4/prompts.py @@ -7,7 +7,10 @@ import textwrap from io import BytesIO from pathlib import Path -from typing import List + +from llama_stack.models.llama.llama4.prompt_templates.system_prompts import ( + PythonListCustomToolGenerator, +) from ..datatypes import RawMediaItem, RawMessage, RawTextItem from ..prompt_format import ( @@ -19,7 +22,7 @@ from ..prompt_format import ( THIS_DIR = Path(__file__).parent -def usecases(base_model: bool = False) -> List[UseCase | str]: +def usecases(base_model: bool = False) -> list[UseCase | str]: with open(THIS_DIR.parent / "resources/small_dog.jpg", "rb") as f: img_small_dog = f.read() with open(THIS_DIR.parent / "resources/dog.jpg", "rb") as f: @@ -177,39 +180,9 @@ def usecases(base_model: bool = False) -> List[UseCase | str]: [ RawMessage( role="system", - content="""You are an expert in composing functions. You are given a question and a set of possible functions. -Based on the question, you will need to make one or more function/tool calls to achieve the purpose. -If none of the function can be used, point it out. If the given question lacks the parameters required by the function, -also point it out. You should only return the function call in tools call sections. - -If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)] -You SHOULD NOT include any other text in the response. - -Here is a list of functions in JSON format that you can invoke. - -[ - { - "name": "get_weather", - "description": "Get weather info for places", - "parameters": { - "type": "dict", - "required": [ - "city" - ], - "properties": { - "city": { - "type": "string", - "description": "The name of the city to get the weather for" - }, - "metric": { - "type": "string", - "description": "The metric for weather. Options are: celsius, fahrenheit", - "default": "celsius" - } - } - } - } -""", + content=PythonListCustomToolGenerator() + .gen(PythonListCustomToolGenerator().data_examples()[0]) + .render(), ), RawMessage( role="user", diff --git a/llama_stack/models/llama/llama4/quantization/loader.py b/llama_stack/models/llama/llama4/quantization/loader.py index f11d83c60..223744a5f 100644 --- a/llama_stack/models/llama/llama4/quantization/loader.py +++ b/llama_stack/models/llama/llama4/quantization/loader.py @@ -6,7 +6,7 @@ import logging import os -from typing import Callable, Optional +from collections.abc import Callable import torch from fairscale.nn.model_parallel.initialize import get_model_parallel_rank @@ -45,8 +45,8 @@ def experts_batched_swiglu_wrapper( def convert_to_quantized_model( model: Transformer, checkpoint_dir: str, - quantization_mode: Optional[str] = None, - fp8_activation_scale_ub: Optional[float] = 1200.0, + quantization_mode: str | None = None, + fp8_activation_scale_ub: float | None = 1200.0, use_rich_progress: bool = True, ) -> Transformer: from ...quantize_impls import ( @@ -213,7 +213,7 @@ def logging_callbacks( ) task_id = progress.add_task("[blue]Converting layers...", total=total_blocks, status="Starting") - def update_status(message: Optional[str], completed: Optional[int] = None) -> None: + def update_status(message: str | None, completed: int | None = None) -> None: if use_rich_progress: if message is not None: progress.update(task_id, status=message) diff --git a/llama_stack/models/llama/llama4/tokenizer.model b/llama_stack/models/llama/llama4/tokenizer.model old mode 100755 new mode 100644 diff --git a/llama_stack/models/llama/llama4/tokenizer.py b/llama_stack/models/llama/llama4/tokenizer.py index 0d2cc7ce5..74070d43e 100644 --- a/llama_stack/models/llama/llama4/tokenizer.py +++ b/llama_stack/models/llama/llama4/tokenizer.py @@ -5,18 +5,11 @@ # the root directory of this source tree. import os +from collections.abc import Collection, Iterator, Sequence, Set from logging import getLogger from pathlib import Path from typing import ( - AbstractSet, - Collection, - Dict, - Iterator, - List, Literal, - Optional, - Sequence, - Union, cast, ) @@ -114,7 +107,7 @@ class Tokenizer: Tokenizing and encoding/decoding text using the Tiktoken tokenizer. """ - special_tokens: Dict[str, int] + special_tokens: dict[str, int] num_reserved_special_tokens = 2048 @@ -182,9 +175,9 @@ class Tokenizer: *, bos: bool, eos: bool, - allowed_special: Optional[Union[Literal["all"], AbstractSet[str]]] = None, - disallowed_special: Union[Literal["all"], Collection[str]] = (), - ) -> List[int]: + allowed_special: Literal["all"] | Set[str] | None = None, + disallowed_special: Literal["all"] | Collection[str] = (), + ) -> list[int]: """ Encodes a string into a list of token IDs. @@ -217,7 +210,7 @@ class Tokenizer: s[i : i + TIKTOKEN_MAX_ENCODE_CHARS], MAX_NO_WHITESPACES_CHARS ) ) - t: List[int] = [] + t: list[int] = [] for substr in substrs: t.extend( self.model.encode( @@ -243,7 +236,7 @@ class Tokenizer: str: The decoded string. """ # Typecast is safe here. Tiktoken doesn't do anything list-related with the sequence. - return self.model.decode(cast(List[int], t)) + return self.model.decode(cast(list[int], t)) @staticmethod def _split_whitespaces_or_nonwhitespaces(s: str, max_consecutive_slice_len: int) -> Iterator[str]: diff --git a/llama_stack/models/llama/llama4/vision/embedding.py b/llama_stack/models/llama/llama4/vision/embedding.py index ed7659a73..c7dd81965 100644 --- a/llama_stack/models/llama/llama4/vision/embedding.py +++ b/llama_stack/models/llama/llama4/vision/embedding.py @@ -5,7 +5,8 @@ # the root directory of this source tree. import math -from typing import Any, Callable, Dict, List +from collections.abc import Callable +from typing import Any import torch import torch.nn as nn @@ -136,13 +137,13 @@ class VisionEmbeddings(torch.nn.Module): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool = True, - missing_keys: List[str] = None, - unexpected_keys: List[str] = None, - error_msgs: List[str] = None, + missing_keys: list[str] = None, + unexpected_keys: list[str] = None, + error_msgs: list[str] = None, return_state_dict: bool = False, ) -> None: original_sd = self.state_dict() @@ -163,7 +164,7 @@ class VisionEmbeddings(torch.nn.Module): # each image is a tensor of shape [num_tiles, C, H, W] def forward( self, - image_batch: List[List[torch.Tensor]], + image_batch: list[list[torch.Tensor]], image_mask: torch.Tensor, h_ref: torch.Tensor, ) -> torch.Tensor: diff --git a/llama_stack/models/llama/llama4/vision/encoder.py b/llama_stack/models/llama/llama4/vision/encoder.py index 4baf03d8d..4b66f1411 100644 --- a/llama_stack/models/llama/llama4/vision/encoder.py +++ b/llama_stack/models/llama/llama4/vision/encoder.py @@ -4,7 +4,8 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from collections.abc import Callable +from typing import Any import fairscale.nn.model_parallel.initialize as fs_init import torch @@ -42,9 +43,9 @@ class ColumnParallelConv2dPatch(torch.nn.Module): self, in_channels: int, out_channels: int, - kernel_size: Union[int, Tuple[int, int]], - stride: Union[int, Tuple[int, int]], - bias: Optional[bool] = False, + kernel_size: int | tuple[int, int], + stride: int | tuple[int, int], + bias: bool | None = False, ) -> None: super().__init__() if isinstance(kernel_size, int): @@ -134,15 +135,15 @@ class _TransformerBlock(nn.Module): def attention( self, x: torch.Tensor, - freq_cis: Optional[torch.Tensor] = None, + freq_cis: torch.Tensor | None = None, ): return self.attn(x=x, start_pos=0, freqs_cis=freq_cis) def forward( self, x: torch.Tensor, - mask: Optional[torch.Tensor] = None, - freq_cis: Optional[torch.Tensor] = None, + mask: torch.Tensor | None = None, + freq_cis: torch.Tensor | None = None, ): _gate_attn = 1 if not self.gated else self.gate_attn.tanh() _gate_ffn = 1 if not self.gated else self.gate_ffn.tanh() @@ -210,8 +211,8 @@ class PackingIndex: class VisionEncoder(nn.Module): def __init__( self, - image_size: Tuple[int, int], - patch_size: Tuple[int, int], + image_size: tuple[int, int], + patch_size: tuple[int, int], dim: int, layers: int, heads: int, @@ -299,13 +300,13 @@ class VisionEncoder(nn.Module): def load_hook( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], prefix: str, - local_metadata: Dict[str, Any], + local_metadata: dict[str, Any], strict: bool = True, - missing_keys: List[str] = None, - unexpected_keys: List[str] = None, - error_msgs: List[str] = None, + missing_keys: list[str] = None, + unexpected_keys: list[str] = None, + error_msgs: list[str] = None, return_state_dict: bool = False, ) -> None: orig_pos_embed = state_dict.get(prefix + "positional_embedding") diff --git a/llama_stack/models/llama/prompt_format.py b/llama_stack/models/llama/prompt_format.py index edb34620c..6191df61a 100644 --- a/llama_stack/models/llama/prompt_format.py +++ b/llama_stack/models/llama/prompt_format.py @@ -14,7 +14,6 @@ import json import textwrap from pathlib import Path -from typing import List from pydantic import BaseModel, Field @@ -44,7 +43,7 @@ class TextCompletionContent(BaseModel): class UseCase(BaseModel): title: str = "" description: str = "" - dialogs: List[List[RawMessage] | TextCompletionContent | str] = Field(default_factory=list) + dialogs: list[list[RawMessage] | TextCompletionContent | str] = Field(default_factory=list) notes: str = "" tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json max_gen_len: int = 512 diff --git a/llama_stack/models/llama/quantize_impls.py b/llama_stack/models/llama/quantize_impls.py index a5da01588..a6400c5c9 100644 --- a/llama_stack/models/llama/quantize_impls.py +++ b/llama_stack/models/llama/quantize_impls.py @@ -7,7 +7,6 @@ # type: ignore import collections import logging -from typing import Optional, Tuple, Type, Union log = logging.getLogger(__name__) @@ -27,7 +26,7 @@ class Fp8ScaledWeights: # TODO: Ugly trick so torch allows us to replace parameters # with our custom Fp8Weights instance. Do this properly. @property - def __class__(self) -> Type[nn.parameter.Parameter]: + def __class__(self) -> type[nn.parameter.Parameter]: return nn.Parameter @property @@ -51,7 +50,7 @@ class Int4ScaledWeights: # TODO: Ugly trick so torch allows us to replace parameters # with our custom Int4Weights instance. Do this properly. @property - def __class__(self) -> Type[nn.parameter.Parameter]: + def __class__(self) -> type[nn.parameter.Parameter]: return nn.Parameter @property @@ -74,7 +73,7 @@ class Int4Weights( def int4_row_quantize( x: torch.Tensor, group_size: int = 128, -) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: n_bit = 4 # Number of target bits. to_quant = x.reshape(-1, group_size).to(torch.float) @@ -115,8 +114,8 @@ def pack_int4(x: torch.Tensor) -> torch.Tensor: def bmm_nt( x: Tensor, - w: Union[Fp8RowwiseWeights, Int4Weights], - num_tokens: Optional[Tensor] = None, + w: Fp8RowwiseWeights | Int4Weights, + num_tokens: Tensor | None = None, ) -> Tensor: if isinstance(w, Fp8ScaledWeights): xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row(x, num_tokens, w.activation_scale_ub) @@ -129,10 +128,10 @@ def bmm_nt( def ffn_swiglu( x: Tensor, - w1: Union[Fp8RowwiseWeights, Int4Weights], - w3: Union[Fp8RowwiseWeights, Int4Weights], - w2: Union[Fp8RowwiseWeights, Int4Weights], - num_tokens: Optional[Tensor] = None, + w1: Fp8RowwiseWeights | Int4Weights, + w3: Fp8RowwiseWeights | Int4Weights, + w2: Fp8RowwiseWeights | Int4Weights, + num_tokens: Tensor | None = None, is_memory_bounded: bool = False, ) -> Tensor: if (isinstance(w1, Fp8ScaledWeights) and isinstance(w3, Fp8ScaledWeights) and isinstance(w2, Fp8ScaledWeights)) or ( @@ -158,7 +157,7 @@ def ffn_swiglu( def quantize_fp8( w: Tensor, fp8_activation_scale_ub: float, - output_device: Optional[torch.device] = None, + output_device: torch.device | None = None, ) -> Fp8RowwiseWeights: """Quantize [n, k] weight tensor. @@ -184,7 +183,7 @@ def quantize_fp8( @torch.inference_mode() def quantize_int4( w: Tensor, - output_device: Optional[torch.device] = None, + output_device: torch.device | None = None, ) -> Int4Weights: """Quantize [n, k/2] weight tensor. @@ -213,7 +212,7 @@ def load_fp8( w: Tensor, w_scale: Tensor, fp8_activation_scale_ub: float, - output_device: Optional[torch.device] = None, + output_device: torch.device | None = None, ) -> Fp8RowwiseWeights: """Load FP8 [n, k] weight tensor. @@ -239,7 +238,7 @@ def load_int4( w: Tensor, scale: Tensor, zero_point: Tensor, - output_device: Optional[torch.device] = None, + output_device: torch.device | None = None, ) -> Int4Weights: """Load INT4 [n, k/2] weight tensor. @@ -256,9 +255,9 @@ def load_int4( def fc_dynamic( x: Tensor, - w: Union[Fp8RowwiseWeights, Int4Weights], - activation_scale_ub: Optional[Tensor] = None, - num_tokens: Optional[Tensor] = None, + w: Fp8RowwiseWeights | Int4Weights, + activation_scale_ub: Tensor | None = None, + num_tokens: Tensor | None = None, is_memory_bounded: bool = False, ) -> Tensor: """ @@ -275,11 +274,11 @@ def fc_dynamic( def ffn_swiglu_dynamic( x: Tensor, - w1: Union[Fp8RowwiseWeights, Int4Weights], - w3: Union[Fp8RowwiseWeights, Int4Weights], - w2: Union[Fp8RowwiseWeights, Int4Weights], - activation_scale_ub: Optional[Tensor] = None, - num_tokens: Optional[Tensor] = None, + w1: Fp8RowwiseWeights | Int4Weights, + w3: Fp8RowwiseWeights | Int4Weights, + w2: Fp8RowwiseWeights | Int4Weights, + activation_scale_ub: Tensor | None = None, + num_tokens: Tensor | None = None, is_memory_bounded: bool = False, ) -> Tensor: assert x.dim() == 3 or x.dim() == 2 diff --git a/llama_stack/models/llama/sku_list.py b/llama_stack/models/llama/sku_list.py index 513481831..a82cbf708 100644 --- a/llama_stack/models/llama/sku_list.py +++ b/llama_stack/models/llama/sku_list.py @@ -6,7 +6,6 @@ from dataclasses import dataclass from functools import lru_cache -from typing import List, Optional from .sku_types import ( CheckpointQuantizationFormat, @@ -19,14 +18,14 @@ LLAMA2_VOCAB_SIZE = 32000 LLAMA3_VOCAB_SIZE = 128256 -def resolve_model(descriptor: str) -> Optional[Model]: +def resolve_model(descriptor: str) -> Model | None: for m in all_registered_models(): if descriptor in (m.descriptor(), m.huggingface_repo): return m return None -def all_registered_models() -> List[Model]: +def all_registered_models() -> list[Model]: return ( llama2_family() + llama3_family() @@ -38,48 +37,48 @@ def all_registered_models() -> List[Model]: ) -def llama2_family() -> List[Model]: +def llama2_family() -> list[Model]: return [ *llama2_base_models(), *llama2_instruct_models(), ] -def llama3_family() -> List[Model]: +def llama3_family() -> list[Model]: return [ *llama3_base_models(), *llama3_instruct_models(), ] -def llama3_1_family() -> List[Model]: +def llama3_1_family() -> list[Model]: return [ *llama3_1_base_models(), *llama3_1_instruct_models(), ] -def llama3_2_family() -> List[Model]: +def llama3_2_family() -> list[Model]: return [ *llama3_2_base_models(), *llama3_2_instruct_models(), ] -def llama3_3_family() -> List[Model]: +def llama3_3_family() -> list[Model]: return [ *llama3_3_instruct_models(), ] -def llama4_family() -> List[Model]: +def llama4_family() -> list[Model]: return [ *llama4_base_models(), *llama4_instruct_models(), ] -def llama4_base_models() -> List[Model]: +def llama4_base_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama4_scout_17b_16e, @@ -98,7 +97,7 @@ def llama4_base_models() -> List[Model]: ] -def llama4_instruct_models() -> List[Model]: +def llama4_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama4_scout_17b_16e_instruct, @@ -126,7 +125,7 @@ def llama4_instruct_models() -> List[Model]: ] -def llama2_base_models() -> List[Model]: +def llama2_base_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama2_7b, @@ -185,7 +184,7 @@ def llama2_base_models() -> List[Model]: ] -def llama3_base_models() -> List[Model]: +def llama3_base_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_8b, @@ -226,7 +225,7 @@ def llama3_base_models() -> List[Model]: ] -def llama3_1_base_models() -> List[Model]: +def llama3_1_base_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_1_8b, @@ -324,7 +323,7 @@ def llama3_1_base_models() -> List[Model]: ] -def llama3_2_base_models() -> List[Model]: +def llama3_2_base_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_2_1b, @@ -407,7 +406,7 @@ def llama3_2_base_models() -> List[Model]: ] -def llama2_instruct_models() -> List[Model]: +def llama2_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama2_7b_chat, @@ -466,7 +465,7 @@ def llama2_instruct_models() -> List[Model]: ] -def llama3_instruct_models() -> List[Model]: +def llama3_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_8b_instruct, @@ -507,7 +506,7 @@ def llama3_instruct_models() -> List[Model]: ] -def llama3_1_instruct_models() -> List[Model]: +def llama3_1_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_1_8b_instruct, @@ -635,7 +634,7 @@ def arch_args_3b() -> dict: } -def llama3_2_quantized_models() -> List[Model]: +def llama3_2_quantized_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_2_1b_instruct, @@ -704,7 +703,7 @@ def llama3_2_quantized_models() -> List[Model]: ] -def llama3_2_instruct_models() -> List[Model]: +def llama3_2_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_2_1b_instruct, @@ -766,7 +765,7 @@ def llama3_2_instruct_models() -> List[Model]: ] -def llama3_3_instruct_models() -> List[Model]: +def llama3_3_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_3_70b_instruct, @@ -790,8 +789,15 @@ def llama3_3_instruct_models() -> List[Model]: @lru_cache -def safety_models() -> List[Model]: +def safety_models() -> list[Model]: return [ + Model( + core_model_id=CoreModelId.llama_guard_4_12b, + description="Llama Guard v4 12b system safety model", + huggingface_repo="meta-llama/Llama-Guard-4-12B", + arch_args={}, + pth_file_count=1, + ), Model( core_model_id=CoreModelId.llama_guard_3_11b_vision, description="Llama Guard v3 11b vision system safety model", @@ -912,7 +918,7 @@ def safety_models() -> List[Model]: @dataclass class LlamaDownloadInfo: folder: str - files: List[str] + files: list[str] pth_size: int diff --git a/llama_stack/models/llama/sku_types.py b/llama_stack/models/llama/sku_types.py index 88799b66d..4147707d5 100644 --- a/llama_stack/models/llama/sku_types.py +++ b/llama_stack/models/llama/sku_types.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, ConfigDict, Field @@ -81,6 +81,7 @@ class CoreModelId(Enum): llama_guard_2_8b = "Llama-Guard-2-8B" llama_guard_3_11b_vision = "Llama-Guard-3-11B-Vision" llama_guard_3_1b = "Llama-Guard-3-1B" + llama_guard_4_12b = "Llama-Guard-4-12B" def is_multimodal(model_id) -> bool: @@ -148,6 +149,7 @@ def model_family(model_id) -> ModelFamily: CoreModelId.llama_guard_2_8b, CoreModelId.llama_guard_3_11b_vision, CoreModelId.llama_guard_3_1b, + CoreModelId.llama_guard_4_12b, ]: return ModelFamily.safety else: @@ -157,13 +159,13 @@ def model_family(model_id) -> ModelFamily: class Model(BaseModel): core_model_id: CoreModelId description: str - huggingface_repo: Optional[str] = None - arch_args: Dict[str, Any] + huggingface_repo: str | None = None + arch_args: dict[str, Any] variant: str = "" quantization_format: CheckpointQuantizationFormat = CheckpointQuantizationFormat.bf16 pth_file_count: int - metadata: Dict[str, Any] = Field(default_factory=dict) + metadata: dict[str, Any] = Field(default_factory=dict) # silence pydantic until we remove the `model_` fields model_config = ConfigDict(protected_namespaces=()) @@ -225,5 +227,7 @@ class Model(BaseModel): CoreModelId.llama_guard_3_1b, ]: return 131072 + elif self.core_model_id == CoreModelId.llama_guard_4_12b: + return 8192 else: raise ValueError(f"Unknown max_seq_len for {self.core_model_id}") diff --git a/llama_stack/providers/datatypes.py b/llama_stack/providers/datatypes.py index c3141f807..3e9806f23 100644 --- a/llama_stack/providers/datatypes.py +++ b/llama_stack/providers/datatypes.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Any, List, Optional, Protocol +from typing import Any, Protocol from urllib.parse import urlparse from pydantic import BaseModel, Field @@ -22,6 +22,27 @@ from llama_stack.schema_utils import json_schema_type class ModelsProtocolPrivate(Protocol): + """ + Protocol for model management. + + This allows users to register their preferred model identifiers. + + Model registration requires - + - a provider, used to route the registration request + - a model identifier, user's intended name for the model during inference + - a provider model identifier, a model identifier supported by the provider + + Providers will only accept registration for provider model ids they support. + + Example, + register: provider x my-model-id x provider-model-id + -> Error if provider does not support provider-model-id + -> Error if my-model-id is already registered + -> Success if provider supports provider-model-id + inference: my-model-id x ... + -> Provider uses provider-model-id for inference + """ + async def register_model(self, model: Model) -> Model: ... async def unregister_model(self, model_id: str) -> None: ... @@ -44,7 +65,7 @@ class DatasetsProtocolPrivate(Protocol): class ScoringFunctionsProtocolPrivate(Protocol): - async def list_scoring_functions(self) -> List[ScoringFn]: ... + async def list_scoring_functions(self) -> list[ScoringFn]: ... async def register_scoring_function(self, scoring_fn: ScoringFn) -> None: ... @@ -67,24 +88,24 @@ class ProviderSpec(BaseModel): ..., description="Fully-qualified classname of the config for this provider", ) - api_dependencies: List[Api] = Field( + api_dependencies: list[Api] = Field( default_factory=list, description="Higher-level API surfaces may depend on other providers to provide their functionality", ) - optional_api_dependencies: List[Api] = Field( + optional_api_dependencies: list[Api] = Field( default_factory=list, ) - deprecation_warning: Optional[str] = Field( + deprecation_warning: str | None = Field( default=None, description="If this provider is deprecated, specify the warning message here", ) - deprecation_error: Optional[str] = Field( + deprecation_error: str | None = Field( default=None, description="If this provider is deprecated and does NOT work, specify the error message here", ) # used internally by the resolver; this is a hack for now - deps__: List[str] = Field(default_factory=list) + deps__: list[str] = Field(default_factory=list) @property def is_sample(self) -> bool: @@ -110,25 +131,25 @@ Fully-qualified name of the module to import. The module is expected to have: - `get_adapter_impl(config, deps)`: returns the adapter implementation """, ) - pip_packages: List[str] = Field( + pip_packages: list[str] = Field( default_factory=list, description="The pip dependencies needed for this implementation", ) config_class: str = Field( description="Fully-qualified classname of the config for this provider", ) - provider_data_validator: Optional[str] = Field( + provider_data_validator: str | None = Field( default=None, ) @json_schema_type class InlineProviderSpec(ProviderSpec): - pip_packages: List[str] = Field( + pip_packages: list[str] = Field( default_factory=list, description="The pip dependencies needed for this implementation", ) - container_image: Optional[str] = Field( + container_image: str | None = Field( default=None, description=""" The container image to use for this implementation. If one is provided, pip_packages will be ignored. @@ -143,14 +164,14 @@ Fully-qualified name of the module to import. The module is expected to have: - `get_provider_impl(config, deps)`: returns the local implementation """, ) - provider_data_validator: Optional[str] = Field( + provider_data_validator: str | None = Field( default=None, ) class RemoteProviderConfig(BaseModel): host: str = "localhost" - port: Optional[int] = None + port: int | None = None protocol: str = "http" @property @@ -176,7 +197,7 @@ API responses, specify the adapter here. ) @property - def container_image(self) -> Optional[str]: + def container_image(self) -> str | None: return None @property @@ -184,16 +205,16 @@ API responses, specify the adapter here. return self.adapter.module @property - def pip_packages(self) -> List[str]: + def pip_packages(self) -> list[str]: return self.adapter.pip_packages @property - def provider_data_validator(self) -> Optional[str]: + def provider_data_validator(self) -> str | None: return self.adapter.provider_data_validator def remote_provider_spec( - api: Api, adapter: AdapterSpec, api_dependencies: Optional[List[Api]] = None + api: Api, adapter: AdapterSpec, api_dependencies: list[Api] | None = None ) -> RemoteProviderSpec: return RemoteProviderSpec( api=api, diff --git a/llama_stack/providers/inline/agents/meta_reference/__init__.py b/llama_stack/providers/inline/agents/meta_reference/__init__.py index 4be064f1d..7503b8c90 100644 --- a/llama_stack/providers/inline/agents/meta_reference/__init__.py +++ b/llama_stack/providers/inline/agents/meta_reference/__init__.py @@ -4,14 +4,14 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.distribution.datatypes import Api from .config import MetaReferenceAgentsImplConfig -async def get_provider_impl(config: MetaReferenceAgentsImplConfig, deps: Dict[Api, Any]): +async def get_provider_impl(config: MetaReferenceAgentsImplConfig, deps: dict[Api, Any]): from .agents import MetaReferenceAgentsImpl impl = MetaReferenceAgentsImpl( diff --git a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py b/llama_stack/providers/inline/agents/meta_reference/agent_instance.py index f441d6eb6..3807ddb86 100644 --- a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py +++ b/llama_stack/providers/inline/agents/meta_reference/agent_instance.py @@ -10,8 +10,8 @@ import re import secrets import string import uuid +from collections.abc import AsyncGenerator from datetime import datetime, timezone -from typing import AsyncGenerator, List, Optional, Union import httpx @@ -112,7 +112,7 @@ class ChatAgent(ShieldRunnerMixin): output_shields=agent_config.output_shields, ) - def turn_to_messages(self, turn: Turn) -> List[Message]: + def turn_to_messages(self, turn: Turn) -> list[Message]: messages = [] # NOTE: if a toolcall response is in a step, we do not add it when processing the input messages @@ -161,7 +161,7 @@ class ChatAgent(ShieldRunnerMixin): async def create_session(self, name: str) -> str: return await self.storage.create_session(name) - async def get_messages_from_turns(self, turns: List[Turn]) -> List[Message]: + async def get_messages_from_turns(self, turns: list[Turn]) -> list[Message]: messages = [] if self.agent_config.instructions != "": messages.append(SystemMessage(content=self.agent_config.instructions)) @@ -178,6 +178,8 @@ class ChatAgent(ShieldRunnerMixin): span.set_attribute("request", request.model_dump_json()) turn_id = str(uuid.uuid4()) span.set_attribute("turn_id", turn_id) + if self.agent_config.name: + span.set_attribute("agent_name", self.agent_config.name) await self._initialize_tools(request.toolgroups) async for chunk in self._run_turn(request, turn_id): @@ -190,6 +192,8 @@ class ChatAgent(ShieldRunnerMixin): span.set_attribute("session_id", request.session_id) span.set_attribute("request", request.model_dump_json()) span.set_attribute("turn_id", request.turn_id) + if self.agent_config.name: + span.set_attribute("agent_name", self.agent_config.name) await self._initialize_tools() async for chunk in self._run_turn(request): @@ -197,8 +201,8 @@ class ChatAgent(ShieldRunnerMixin): async def _run_turn( self, - request: Union[AgentTurnCreateRequest, AgentTurnResumeRequest], - turn_id: Optional[str] = None, + request: AgentTurnCreateRequest | AgentTurnResumeRequest, + turn_id: str | None = None, ) -> AsyncGenerator: assert request.stream is True, "Non-streaming not supported" @@ -317,10 +321,10 @@ class ChatAgent(ShieldRunnerMixin): self, session_id: str, turn_id: str, - input_messages: List[Message], + input_messages: list[Message], sampling_params: SamplingParams, stream: bool = False, - documents: Optional[List[Document]] = None, + documents: list[Document] | None = None, ) -> AsyncGenerator: # Doing async generators makes downstream code much simpler and everything amenable to # streaming. However, it also makes things complicated here because AsyncGenerators cannot @@ -370,8 +374,8 @@ class ChatAgent(ShieldRunnerMixin): async def run_multiple_shields_wrapper( self, turn_id: str, - messages: List[Message], - shields: List[str], + messages: list[Message], + shields: list[str], touchpoint: str, ) -> AsyncGenerator: async with tracing.span("run_shields") as span: @@ -439,10 +443,10 @@ class ChatAgent(ShieldRunnerMixin): self, session_id: str, turn_id: str, - input_messages: List[Message], + input_messages: list[Message], sampling_params: SamplingParams, stream: bool = False, - documents: Optional[List[Document]] = None, + documents: list[Document] | None = None, ) -> AsyncGenerator: # if document is passed in a turn, we parse the raw text of the document # and sent it as a user message @@ -498,6 +502,8 @@ class ChatAgent(ShieldRunnerMixin): stop_reason = None async with tracing.span("inference") as span: + if self.agent_config.name: + span.set_attribute("agent_name", self.agent_config.name) async for chunk in await self.inference_api.chat_completion( self.agent_config.model, input_messages, @@ -754,7 +760,7 @@ class ChatAgent(ShieldRunnerMixin): async def _initialize_tools( self, - toolgroups_for_turn: Optional[List[AgentToolGroup]] = None, + toolgroups_for_turn: list[AgentToolGroup] | None = None, ) -> None: toolgroup_to_args = {} for toolgroup in (self.agent_config.toolgroups or []) + (toolgroups_for_turn or []): @@ -841,7 +847,7 @@ class ChatAgent(ShieldRunnerMixin): tool_name_to_args, ) - def _parse_toolgroup_name(self, toolgroup_name_with_maybe_tool_name: str) -> tuple[str, Optional[str]]: + def _parse_toolgroup_name(self, toolgroup_name_with_maybe_tool_name: str) -> tuple[str, str | None]: """Parse a toolgroup name into its components. Args: @@ -915,7 +921,7 @@ async def get_raw_document_text(document: Document) -> str: def _interpret_content_as_attachment( content: str, -) -> Optional[Attachment]: +) -> Attachment | None: match = re.search(TOOLS_ATTACHMENT_KEY_REGEX, content) if match: snippet = match.group(1) diff --git a/llama_stack/providers/inline/agents/meta_reference/agents.py b/llama_stack/providers/inline/agents/meta_reference/agents.py index 656178773..a87739925 100644 --- a/llama_stack/providers/inline/agents/meta_reference/agents.py +++ b/llama_stack/providers/inline/agents/meta_reference/agents.py @@ -6,9 +6,8 @@ import json import logging -import shutil import uuid -from typing import AsyncGenerator, List, Optional, Union +from collections.abc import AsyncGenerator from llama_stack.apis.agents import ( Agent, @@ -23,6 +22,9 @@ from llama_stack.apis.agents import ( Document, ListAgentSessionsResponse, ListAgentsResponse, + OpenAIResponseInputMessage, + OpenAIResponseInputTool, + OpenAIResponseObject, Session, Turn, ) @@ -40,6 +42,7 @@ from llama_stack.providers.utils.kvstore import InmemoryKVStoreImpl, kvstore_imp from .agent_instance import ChatAgent from .config import MetaReferenceAgentsImplConfig +from .openai_responses import OpenAIResponsesImpl logger = logging.getLogger() logger.setLevel(logging.INFO) @@ -63,13 +66,16 @@ class MetaReferenceAgentsImpl(Agents): self.tool_groups_api = tool_groups_api self.in_memory_store = InmemoryKVStoreImpl() + self.openai_responses_impl = None async def initialize(self) -> None: self.persistence_store = await kvstore_impl(self.config.persistence_store) - - # check if "bwrap" is available - if not shutil.which("bwrap"): - logger.warning("Warning: `bwrap` is not available. Code interpreter tool will not work correctly.") + self.openai_responses_impl = OpenAIResponsesImpl( + self.persistence_store, + inference_api=self.inference_api, + tool_groups_api=self.tool_groups_api, + tool_runtime_api=self.tool_runtime_api, + ) async def create_agent( self, @@ -131,16 +137,11 @@ class MetaReferenceAgentsImpl(Agents): self, agent_id: str, session_id: str, - messages: List[ - Union[ - UserMessage, - ToolResponseMessage, - ] - ], - toolgroups: Optional[List[AgentToolGroup]] = None, - documents: Optional[List[Document]] = None, - stream: Optional[bool] = False, - tool_config: Optional[ToolConfig] = None, + messages: list[UserMessage | ToolResponseMessage], + toolgroups: list[AgentToolGroup] | None = None, + documents: list[Document] | None = None, + stream: bool | None = False, + tool_config: ToolConfig | None = None, ) -> AsyncGenerator: request = AgentTurnCreateRequest( agent_id=agent_id, @@ -169,8 +170,8 @@ class MetaReferenceAgentsImpl(Agents): agent_id: str, session_id: str, turn_id: str, - tool_responses: List[ToolResponse], - stream: Optional[bool] = False, + tool_responses: list[ToolResponse], + stream: bool | None = False, ) -> AsyncGenerator: request = AgentTurnResumeRequest( agent_id=agent_id, @@ -208,7 +209,7 @@ class MetaReferenceAgentsImpl(Agents): self, agent_id: str, session_id: str, - turn_ids: Optional[List[str]] = None, + turn_ids: list[str] | None = None, ) -> Session: agent = await self._get_agent_impl(agent_id) session_info = await agent.storage.get_session_info(session_id) @@ -244,3 +245,24 @@ class MetaReferenceAgentsImpl(Agents): agent_id: str, ) -> ListAgentSessionsResponse: pass + + # OpenAI responses + async def get_openai_response( + self, + id: str, + ) -> OpenAIResponseObject: + return await self.openai_responses_impl.get_openai_response(id) + + async def create_openai_response( + self, + input: str | list[OpenAIResponseInputMessage], + model: str, + previous_response_id: str | None = None, + store: bool | None = True, + stream: bool | None = False, + temperature: float | None = None, + tools: list[OpenAIResponseInputTool] | None = None, + ) -> OpenAIResponseObject: + return await self.openai_responses_impl.create_openai_response( + input, model, previous_response_id, store, stream, temperature, tools + ) diff --git a/llama_stack/providers/inline/agents/meta_reference/config.py b/llama_stack/providers/inline/agents/meta_reference/config.py index ff34e5d5f..c860e6df1 100644 --- a/llama_stack/providers/inline/agents/meta_reference/config.py +++ b/llama_stack/providers/inline/agents/meta_reference/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -16,7 +16,7 @@ class MetaReferenceAgentsImplConfig(BaseModel): persistence_store: KVStoreConfig @classmethod - def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]: return { "persistence_store": SqliteKVStoreConfig.sample_run_config( __distro_dir__=__distro_dir__, diff --git a/llama_stack/providers/inline/agents/meta_reference/openai_responses.py b/llama_stack/providers/inline/agents/meta_reference/openai_responses.py new file mode 100644 index 000000000..24a99dd6e --- /dev/null +++ b/llama_stack/providers/inline/agents/meta_reference/openai_responses.py @@ -0,0 +1,328 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import json +import uuid +from collections.abc import AsyncIterator +from typing import cast + +from openai.types.chat import ChatCompletionToolParam + +from llama_stack.apis.agents.openai_responses import ( + OpenAIResponseInputMessage, + OpenAIResponseInputMessageContentImage, + OpenAIResponseInputMessageContentText, + OpenAIResponseInputTool, + OpenAIResponseObject, + OpenAIResponseObjectStream, + OpenAIResponseObjectStreamResponseCompleted, + OpenAIResponseObjectStreamResponseCreated, + OpenAIResponseOutput, + OpenAIResponseOutputMessage, + OpenAIResponseOutputMessageContentOutputText, + OpenAIResponseOutputMessageWebSearchToolCall, +) +from llama_stack.apis.inference.inference import ( + Inference, + OpenAIAssistantMessageParam, + OpenAIChatCompletion, + OpenAIChatCompletionContentPartImageParam, + OpenAIChatCompletionContentPartParam, + OpenAIChatCompletionContentPartTextParam, + OpenAIChatCompletionToolCallFunction, + OpenAIChoice, + OpenAIImageURL, + OpenAIMessageParam, + OpenAIToolMessageParam, + OpenAIUserMessageParam, +) +from llama_stack.apis.tools.tools import ToolGroups, ToolInvocationResult, ToolRuntime +from llama_stack.log import get_logger +from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition +from llama_stack.providers.utils.inference.openai_compat import convert_tooldef_to_openai_tool +from llama_stack.providers.utils.kvstore import KVStore + +logger = get_logger(name=__name__, category="openai_responses") + +OPENAI_RESPONSES_PREFIX = "openai_responses:" + + +async def _previous_response_to_messages(previous_response: OpenAIResponseObject) -> list[OpenAIMessageParam]: + messages: list[OpenAIMessageParam] = [] + for output_message in previous_response.output: + if isinstance(output_message, OpenAIResponseOutputMessage): + messages.append(OpenAIAssistantMessageParam(content=output_message.content[0].text)) + return messages + + +async def _openai_choices_to_output_messages(choices: list[OpenAIChoice]) -> list[OpenAIResponseOutputMessage]: + output_messages = [] + for choice in choices: + output_content = "" + if isinstance(choice.message.content, str): + output_content = choice.message.content + elif isinstance(choice.message.content, OpenAIChatCompletionContentPartTextParam): + output_content = choice.message.content.text + # TODO: handle image content + output_messages.append( + OpenAIResponseOutputMessage( + id=f"msg_{uuid.uuid4()}", + content=[OpenAIResponseOutputMessageContentOutputText(text=output_content)], + status="completed", + ) + ) + return output_messages + + +class OpenAIResponsesImpl: + def __init__( + self, + persistence_store: KVStore, + inference_api: Inference, + tool_groups_api: ToolGroups, + tool_runtime_api: ToolRuntime, + ): + self.persistence_store = persistence_store + self.inference_api = inference_api + self.tool_groups_api = tool_groups_api + self.tool_runtime_api = tool_runtime_api + + async def get_openai_response( + self, + id: str, + ) -> OpenAIResponseObject: + key = f"{OPENAI_RESPONSES_PREFIX}{id}" + response_json = await self.persistence_store.get(key=key) + if response_json is None: + raise ValueError(f"OpenAI response with id '{id}' not found") + return OpenAIResponseObject.model_validate_json(response_json) + + async def create_openai_response( + self, + input: str | list[OpenAIResponseInputMessage], + model: str, + previous_response_id: str | None = None, + store: bool | None = True, + stream: bool | None = False, + temperature: float | None = None, + tools: list[OpenAIResponseInputTool] | None = None, + ): + stream = False if stream is None else stream + + messages: list[OpenAIMessageParam] = [] + if previous_response_id: + previous_response = await self.get_openai_response(previous_response_id) + messages.extend(await _previous_response_to_messages(previous_response)) + # TODO: refactor this user_content parsing out into a separate method + user_content: str | list[OpenAIChatCompletionContentPartParam] = "" + if isinstance(input, list): + user_content = [] + for user_input in input: + if isinstance(user_input.content, list): + for user_input_content in user_input.content: + if isinstance(user_input_content, OpenAIResponseInputMessageContentText): + user_content.append(OpenAIChatCompletionContentPartTextParam(text=user_input_content.text)) + elif isinstance(user_input_content, OpenAIResponseInputMessageContentImage): + if user_input_content.image_url: + image_url = OpenAIImageURL( + url=user_input_content.image_url, detail=user_input_content.detail + ) + user_content.append(OpenAIChatCompletionContentPartImageParam(image_url=image_url)) + else: + user_content.append(OpenAIChatCompletionContentPartTextParam(text=user_input.content)) + else: + user_content = input + messages.append(OpenAIUserMessageParam(content=user_content)) + + chat_tools = await self._convert_response_tools_to_chat_tools(tools) if tools else None + chat_response = await self.inference_api.openai_chat_completion( + model=model, + messages=messages, + tools=chat_tools, + stream=stream, + temperature=temperature, + ) + + if stream: + # TODO: refactor this into a separate method that handles streaming + chat_response_id = "" + chat_response_content = [] + # TODO: these chunk_ fields are hacky and only take the last chunk into account + chunk_created = 0 + chunk_model = "" + chunk_finish_reason = "" + async for chunk in chat_response: + chat_response_id = chunk.id + chunk_created = chunk.created + chunk_model = chunk.model + for chunk_choice in chunk.choices: + # TODO: this only works for text content + chat_response_content.append(chunk_choice.delta.content or "") + if chunk_choice.finish_reason: + chunk_finish_reason = chunk_choice.finish_reason + assistant_message = OpenAIAssistantMessageParam(content="".join(chat_response_content)) + chat_response = OpenAIChatCompletion( + id=chat_response_id, + choices=[ + OpenAIChoice( + message=assistant_message, + finish_reason=chunk_finish_reason, + index=0, + ) + ], + created=chunk_created, + model=chunk_model, + ) + else: + # dump and reload to map to our pydantic types + chat_response = OpenAIChatCompletion(**chat_response.model_dump()) + + output_messages: list[OpenAIResponseOutput] = [] + if chat_response.choices[0].message.tool_calls: + output_messages.extend( + await self._execute_tool_and_return_final_output(model, stream, chat_response, messages, temperature) + ) + else: + output_messages.extend(await _openai_choices_to_output_messages(chat_response.choices)) + response = OpenAIResponseObject( + created_at=chat_response.created, + id=f"resp-{uuid.uuid4()}", + model=model, + object="response", + status="completed", + output=output_messages, + ) + + if store: + # Store in kvstore + key = f"{OPENAI_RESPONSES_PREFIX}{response.id}" + await self.persistence_store.set( + key=key, + value=response.model_dump_json(), + ) + + if stream: + + async def async_response() -> AsyncIterator[OpenAIResponseObjectStream]: + # TODO: response created should actually get emitted much earlier in the process + yield OpenAIResponseObjectStreamResponseCreated(response=response) + yield OpenAIResponseObjectStreamResponseCompleted(response=response) + + return async_response() + + return response + + async def _convert_response_tools_to_chat_tools( + self, tools: list[OpenAIResponseInputTool] + ) -> list[ChatCompletionToolParam]: + chat_tools: list[ChatCompletionToolParam] = [] + for input_tool in tools: + # TODO: Handle other tool types + if input_tool.type == "web_search": + tool_name = "web_search" + tool = await self.tool_groups_api.get_tool(tool_name) + tool_def = ToolDefinition( + tool_name=tool_name, + description=tool.description, + parameters={ + param.name: ToolParamDefinition( + param_type=param.parameter_type, + description=param.description, + required=param.required, + default=param.default, + ) + for param in tool.parameters + }, + ) + chat_tool = convert_tooldef_to_openai_tool(tool_def) + chat_tools.append(chat_tool) + else: + raise ValueError(f"Llama Stack OpenAI Responses does not yet support tool type: {input_tool.type}") + return chat_tools + + async def _execute_tool_and_return_final_output( + self, + model_id: str, + stream: bool, + chat_response: OpenAIChatCompletion, + messages: list[OpenAIMessageParam], + temperature: float, + ) -> list[OpenAIResponseOutput]: + output_messages: list[OpenAIResponseOutput] = [] + choice = chat_response.choices[0] + + # If the choice is not an assistant message, we don't need to execute any tools + if not isinstance(choice.message, OpenAIAssistantMessageParam): + return output_messages + + # If the assistant message doesn't have any tool calls, we don't need to execute any tools + if not choice.message.tool_calls: + return output_messages + + # Add the assistant message with tool_calls response to the messages list + messages.append(choice.message) + + for tool_call in choice.message.tool_calls: + tool_call_id = tool_call.id + function = tool_call.function + + # If for some reason the tool call doesn't have a function or id, we can't execute it + if not function or not tool_call_id: + continue + + # TODO: telemetry spans for tool calls + result = await self._execute_tool_call(function) + + # Handle tool call failure + if not result: + output_messages.append( + OpenAIResponseOutputMessageWebSearchToolCall( + id=tool_call_id, + status="failed", + ) + ) + continue + + output_messages.append( + OpenAIResponseOutputMessageWebSearchToolCall( + id=tool_call_id, + status="completed", + ), + ) + + result_content = "" + # TODO: handle other result content types and lists + if isinstance(result.content, str): + result_content = result.content + messages.append(OpenAIToolMessageParam(content=result_content, tool_call_id=tool_call_id)) + + tool_results_chat_response = await self.inference_api.openai_chat_completion( + model=model_id, + messages=messages, + stream=stream, + temperature=temperature, + ) + # type cast to appease mypy + tool_results_chat_response = cast(OpenAIChatCompletion, tool_results_chat_response) + tool_final_outputs = await _openai_choices_to_output_messages(tool_results_chat_response.choices) + # TODO: Wire in annotations with URLs, titles, etc to these output messages + output_messages.extend(tool_final_outputs) + return output_messages + + async def _execute_tool_call( + self, + function: OpenAIChatCompletionToolCallFunction, + ) -> ToolInvocationResult | None: + if not function.name: + return None + function_args = json.loads(function.arguments) if function.arguments else {} + logger.info(f"executing tool call: {function.name} with args: {function_args}") + result = await self.tool_runtime_api.invoke_tool( + tool_name=function.name, + kwargs=function_args, + ) + logger.debug(f"tool call {function.name} completed with result: {result}") + return result diff --git a/llama_stack/providers/inline/agents/meta_reference/persistence.py b/llama_stack/providers/inline/agents/meta_reference/persistence.py index 202d43609..60ce91395 100644 --- a/llama_stack/providers/inline/agents/meta_reference/persistence.py +++ b/llama_stack/providers/inline/agents/meta_reference/persistence.py @@ -8,7 +8,6 @@ import json import logging import uuid from datetime import datetime, timezone -from typing import List, Optional from pydantic import BaseModel @@ -25,9 +24,9 @@ class AgentSessionInfo(BaseModel): session_id: str session_name: str # TODO: is this used anywhere? - vector_db_id: Optional[str] = None + vector_db_id: str | None = None started_at: datetime - access_attributes: Optional[AccessAttributes] = None + access_attributes: AccessAttributes | None = None class AgentPersistence: @@ -55,7 +54,7 @@ class AgentPersistence: ) return session_id - async def get_session_info(self, session_id: str) -> Optional[AgentSessionInfo]: + async def get_session_info(self, session_id: str) -> AgentSessionInfo | None: value = await self.kvstore.get( key=f"session:{self.agent_id}:{session_id}", ) @@ -78,7 +77,7 @@ class AgentPersistence: return check_access(session_info.session_id, session_info.access_attributes, get_auth_attributes()) - async def get_session_if_accessible(self, session_id: str) -> Optional[AgentSessionInfo]: + async def get_session_if_accessible(self, session_id: str) -> AgentSessionInfo | None: """Get session info if the user has access to it. For internal use by sub-session methods.""" session_info = await self.get_session_info(session_id) if not session_info: @@ -106,7 +105,7 @@ class AgentPersistence: value=turn.model_dump_json(), ) - async def get_session_turns(self, session_id: str) -> List[Turn]: + async def get_session_turns(self, session_id: str) -> list[Turn]: if not await self.get_session_if_accessible(session_id): raise ValueError(f"Session {session_id} not found or access denied") @@ -125,7 +124,7 @@ class AgentPersistence: turns.sort(key=lambda x: (x.completed_at or datetime.min)) return turns - async def get_session_turn(self, session_id: str, turn_id: str) -> Optional[Turn]: + async def get_session_turn(self, session_id: str, turn_id: str) -> Turn | None: if not await self.get_session_if_accessible(session_id): raise ValueError(f"Session {session_id} not found or access denied") @@ -145,7 +144,7 @@ class AgentPersistence: value=step.model_dump_json(), ) - async def get_in_progress_tool_call_step(self, session_id: str, turn_id: str) -> Optional[ToolExecutionStep]: + async def get_in_progress_tool_call_step(self, session_id: str, turn_id: str) -> ToolExecutionStep | None: if not await self.get_session_if_accessible(session_id): return None @@ -163,7 +162,7 @@ class AgentPersistence: value=str(num_infer_iters), ) - async def get_num_infer_iters_in_turn(self, session_id: str, turn_id: str) -> Optional[int]: + async def get_num_infer_iters_in_turn(self, session_id: str, turn_id: str) -> int | None: if not await self.get_session_if_accessible(session_id): return None diff --git a/llama_stack/providers/inline/agents/meta_reference/safety.py b/llama_stack/providers/inline/agents/meta_reference/safety.py index bef16eaba..6b3573d8c 100644 --- a/llama_stack/providers/inline/agents/meta_reference/safety.py +++ b/llama_stack/providers/inline/agents/meta_reference/safety.py @@ -6,7 +6,6 @@ import asyncio import logging -from typing import List from llama_stack.apis.inference import Message from llama_stack.apis.safety import Safety, SafetyViolation, ViolationLevel @@ -25,14 +24,14 @@ class ShieldRunnerMixin: def __init__( self, safety_api: Safety, - input_shields: List[str] = None, - output_shields: List[str] = None, + input_shields: list[str] = None, + output_shields: list[str] = None, ): self.safety_api = safety_api self.input_shields = input_shields self.output_shields = output_shields - async def run_multiple_shields(self, messages: List[Message], identifiers: List[str]) -> None: + async def run_multiple_shields(self, messages: list[Message], identifiers: list[str]) -> None: async def run_shield_with_span(identifier: str): async with tracing.span(f"run_shield_{identifier}"): return await self.safety_api.run_shield( diff --git a/llama_stack/providers/inline/datasetio/localfs/__init__.py b/llama_stack/providers/inline/datasetio/localfs/__init__.py index 5a0876d79..58aa6ffaf 100644 --- a/llama_stack/providers/inline/datasetio/localfs/__init__.py +++ b/llama_stack/providers/inline/datasetio/localfs/__init__.py @@ -4,14 +4,14 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from .config import LocalFSDatasetIOConfig async def get_provider_impl( config: LocalFSDatasetIOConfig, - _deps: Dict[str, Any], + _deps: dict[str, Any], ): from .datasetio import LocalFSDatasetIOImpl diff --git a/llama_stack/providers/inline/datasetio/localfs/config.py b/llama_stack/providers/inline/datasetio/localfs/config.py index d74521f1f..b450e8777 100644 --- a/llama_stack/providers/inline/datasetio/localfs/config.py +++ b/llama_stack/providers/inline/datasetio/localfs/config.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -17,7 +17,7 @@ class LocalFSDatasetIOConfig(BaseModel): kvstore: KVStoreConfig @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return { "kvstore": SqliteKVStoreConfig.sample_run_config( __distro_dir__=__distro_dir__, diff --git a/llama_stack/providers/inline/datasetio/localfs/datasetio.py b/llama_stack/providers/inline/datasetio/localfs/datasetio.py index e71107d61..260a640bd 100644 --- a/llama_stack/providers/inline/datasetio/localfs/datasetio.py +++ b/llama_stack/providers/inline/datasetio/localfs/datasetio.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Optional +from typing import Any import pandas @@ -92,8 +92,8 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate): async def iterrows( self, dataset_id: str, - start_index: Optional[int] = None, - limit: Optional[int] = None, + start_index: int | None = None, + limit: int | None = None, ) -> PaginatedResponse: dataset_def = self.dataset_infos[dataset_id] dataset_impl = PandasDataframeDataset(dataset_def) @@ -102,7 +102,7 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate): records = dataset_impl.df.to_dict("records") return paginate_records(records, start_index, limit) - async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None: + async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None: dataset_def = self.dataset_infos[dataset_id] dataset_impl = PandasDataframeDataset(dataset_def) await dataset_impl.load() diff --git a/llama_stack/providers/inline/eval/meta_reference/__init__.py b/llama_stack/providers/inline/eval/meta_reference/__init__.py index e2a7fc2cd..7afe7f33b 100644 --- a/llama_stack/providers/inline/eval/meta_reference/__init__.py +++ b/llama_stack/providers/inline/eval/meta_reference/__init__.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.distribution.datatypes import Api @@ -12,7 +12,7 @@ from .config import MetaReferenceEvalConfig async def get_provider_impl( config: MetaReferenceEvalConfig, - deps: Dict[Api, Any], + deps: dict[Api, Any], ): from .eval import MetaReferenceEvalImpl diff --git a/llama_stack/providers/inline/eval/meta_reference/config.py b/llama_stack/providers/inline/eval/meta_reference/config.py index 5b2bec259..2a4a29998 100644 --- a/llama_stack/providers/inline/eval/meta_reference/config.py +++ b/llama_stack/providers/inline/eval/meta_reference/config.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -17,7 +17,7 @@ class MetaReferenceEvalConfig(BaseModel): kvstore: KVStoreConfig @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return { "kvstore": SqliteKVStoreConfig.sample_run_config( __distro_dir__=__distro_dir__, diff --git a/llama_stack/providers/inline/eval/meta_reference/eval.py b/llama_stack/providers/inline/eval/meta_reference/eval.py index 7c28f1bb7..12dde66b5 100644 --- a/llama_stack/providers/inline/eval/meta_reference/eval.py +++ b/llama_stack/providers/inline/eval/meta_reference/eval.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. import json -from typing import Any, Dict, List +from typing import Any from tqdm import tqdm @@ -105,8 +105,8 @@ class MetaReferenceEvalImpl( return Job(job_id=job_id, status=JobStatus.completed) async def _run_agent_generation( - self, input_rows: List[Dict[str, Any]], benchmark_config: BenchmarkConfig - ) -> List[Dict[str, Any]]: + self, input_rows: list[dict[str, Any]], benchmark_config: BenchmarkConfig + ) -> list[dict[str, Any]]: candidate = benchmark_config.eval_candidate create_response = await self.agents_api.create_agent(candidate.config) agent_id = create_response.agent_id @@ -148,8 +148,8 @@ class MetaReferenceEvalImpl( return generations async def _run_model_generation( - self, input_rows: List[Dict[str, Any]], benchmark_config: BenchmarkConfig - ) -> List[Dict[str, Any]]: + self, input_rows: list[dict[str, Any]], benchmark_config: BenchmarkConfig + ) -> list[dict[str, Any]]: candidate = benchmark_config.eval_candidate assert candidate.sampling_params.max_tokens is not None, "SamplingParams.max_tokens must be provided" @@ -185,8 +185,8 @@ class MetaReferenceEvalImpl( async def evaluate_rows( self, benchmark_id: str, - input_rows: List[Dict[str, Any]], - scoring_functions: List[str], + input_rows: list[dict[str, Any]], + scoring_functions: list[str], benchmark_config: BenchmarkConfig, ) -> EvaluateResponse: candidate = benchmark_config.eval_candidate diff --git a/llama_stack/providers/inline/inference/meta_reference/__init__.py b/llama_stack/providers/inline/inference/meta_reference/__init__.py index 3710766e2..5eb822429 100644 --- a/llama_stack/providers/inline/inference/meta_reference/__init__.py +++ b/llama_stack/providers/inline/inference/meta_reference/__init__.py @@ -4,14 +4,14 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from .config import MetaReferenceInferenceConfig async def get_provider_impl( config: MetaReferenceInferenceConfig, - _deps: Dict[str, Any], + _deps: dict[str, Any], ): from .inference import MetaReferenceInferenceImpl diff --git a/llama_stack/providers/inline/inference/meta_reference/config.py b/llama_stack/providers/inline/inference/meta_reference/config.py index 6f796d0d4..7bc961443 100644 --- a/llama_stack/providers/inline/inference/meta_reference/config.py +++ b/llama_stack/providers/inline/inference/meta_reference/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, field_validator @@ -17,11 +17,11 @@ class MetaReferenceInferenceConfig(BaseModel): # the actual inference model id is dtermined by the moddel id in the request # Note: you need to register the model before using it for inference # models in the resouce list in the run.yaml config will be registered automatically - model: Optional[str] = None - torch_seed: Optional[int] = None + model: str | None = None + torch_seed: int | None = None max_seq_len: int = 4096 max_batch_size: int = 1 - model_parallel_size: Optional[int] = None + model_parallel_size: int | None = None # when this is False, we assume that the distributed process group is setup by someone # outside of this code (e.g., when run inside `torchrun`). that is useful for clients @@ -30,9 +30,9 @@ class MetaReferenceInferenceConfig(BaseModel): # By default, the implementation will look at ~/.llama/checkpoints/ but you # can override by specifying the directory explicitly - checkpoint_dir: Optional[str] = None + checkpoint_dir: str | None = None - quantization: Optional[QuantizationConfig] = None + quantization: QuantizationConfig | None = None @field_validator("model") @classmethod @@ -55,7 +55,7 @@ class MetaReferenceInferenceConfig(BaseModel): max_batch_size: str = "${env.MAX_BATCH_SIZE:1}", max_seq_len: str = "${env.MAX_SEQ_LEN:4096}", **kwargs, - ) -> Dict[str, Any]: + ) -> dict[str, Any]: return { "model": model, "checkpoint_dir": checkpoint_dir, diff --git a/llama_stack/providers/inline/inference/meta_reference/generators.py b/llama_stack/providers/inline/inference/meta_reference/generators.py index 0a928ce73..cb926f529 100644 --- a/llama_stack/providers/inline/inference/meta_reference/generators.py +++ b/llama_stack/providers/inline/inference/meta_reference/generators.py @@ -5,7 +5,8 @@ # the root directory of this source tree. import math -from typing import Generator, List, Optional, Tuple +from collections.abc import Generator +from typing import Optional import torch from lmformatenforcer import JsonSchemaParser, TokenEnforcer, TokenEnforcerTokenizerData @@ -39,7 +40,7 @@ Tokenizer = Llama4Tokenizer | Llama3Tokenizer class LogitsProcessor: def __init__(self, token_enforcer: TokenEnforcer): self.token_enforcer = token_enforcer - self.mask: Optional[torch.Tensor] = None + self.mask: torch.Tensor | None = None def __call__(self, tokens: torch.Tensor, scores: torch.Tensor) -> torch.Tensor: token_sequence = tokens[0, :].tolist() @@ -58,7 +59,7 @@ class LogitsProcessor: def get_logits_processor( tokenizer: Tokenizer, vocab_size: int, - response_format: Optional[ResponseFormat], + response_format: ResponseFormat | None, ) -> Optional["LogitsProcessor"]: if response_format is None: return None @@ -76,7 +77,7 @@ def get_logits_processor( return LogitsProcessor(token_enforcer) -def _build_regular_tokens_list(tokenizer: Tokenizer, vocab_size: int) -> List[Tuple[int, str, bool]]: +def _build_regular_tokens_list(tokenizer: Tokenizer, vocab_size: int) -> list[tuple[int, str, bool]]: token_0 = tokenizer.encode("0", bos=False, eos=False)[-1] regular_tokens = [] @@ -158,7 +159,7 @@ class LlamaGenerator: def completion( self, - request_batch: List[CompletionRequestWithRawContent], + request_batch: list[CompletionRequestWithRawContent], ) -> Generator: first_request = request_batch[0] sampling_params = first_request.sampling_params or SamplingParams() @@ -167,7 +168,7 @@ class LlamaGenerator: max_gen_len = self.args.max_seq_len - 1 temperature, top_p = _infer_sampling_params(sampling_params) - for result in self.inner_generator.generate( + yield from self.inner_generator.generate( llm_inputs=[self.formatter.encode_content(request.content) for request in request_batch], max_gen_len=max_gen_len, temperature=temperature, @@ -179,12 +180,11 @@ class LlamaGenerator: self.args.vocab_size, first_request.response_format, ), - ): - yield result + ) def chat_completion( self, - request_batch: List[ChatCompletionRequestWithRawContent], + request_batch: list[ChatCompletionRequestWithRawContent], ) -> Generator: first_request = request_batch[0] sampling_params = first_request.sampling_params or SamplingParams() @@ -193,7 +193,7 @@ class LlamaGenerator: max_gen_len = self.args.max_seq_len - 1 temperature, top_p = _infer_sampling_params(sampling_params) - for result in self.inner_generator.generate( + yield from self.inner_generator.generate( llm_inputs=[ self.formatter.encode_dialog_prompt(request.messages, _infer_tool_prompt_format(request)) for request in request_batch @@ -208,5 +208,4 @@ class LlamaGenerator: self.args.vocab_size, first_request.response_format, ), - ): - yield result + ) diff --git a/llama_stack/providers/inline/inference/meta_reference/inference.py b/llama_stack/providers/inline/inference/meta_reference/inference.py index 2b9a27982..8dd594869 100644 --- a/llama_stack/providers/inline/inference/meta_reference/inference.py +++ b/llama_stack/providers/inline/inference/meta_reference/inference.py @@ -6,7 +6,7 @@ import asyncio import os -from typing import AsyncGenerator, List, Optional, Union +from collections.abc import AsyncGenerator from pydantic import BaseModel from termcolor import cprint @@ -184,11 +184,11 @@ class MetaReferenceInferenceImpl( self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - ) -> Union[CompletionResponse, CompletionResponseStreamChunk]: + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + ) -> CompletionResponse | CompletionResponseStreamChunk: if sampling_params is None: sampling_params = SamplingParams() if logprobs: @@ -215,11 +215,11 @@ class MetaReferenceInferenceImpl( async def batch_completion( self, model_id: str, - content_batch: List[InterleavedContent], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + content_batch: list[InterleavedContent], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> BatchCompletionResponse: if sampling_params is None: sampling_params = SamplingParams() @@ -253,7 +253,8 @@ class MetaReferenceInferenceImpl( def impl(): stop_reason = None - for token_result in self.generator.completion(request): + for token_results in self.generator.completion([request]): + token_result = token_results[0] if token_result.token == tokenizer.eot_id: stop_reason = StopReason.end_of_turn text = "" @@ -290,14 +291,14 @@ class MetaReferenceInferenceImpl( for x in impl(): yield x - async def _nonstream_completion(self, request_batch: List[CompletionRequest]) -> List[CompletionResponse]: + async def _nonstream_completion(self, request_batch: list[CompletionRequest]) -> list[CompletionResponse]: tokenizer = self.generator.formatter.tokenizer first_request = request_batch[0] class ItemState(BaseModel): - tokens: List[int] = [] - logprobs: List[TokenLogProbs] = [] + tokens: list[int] = [] + logprobs: list[TokenLogProbs] = [] stop_reason: StopReason | None = None finished: bool = False @@ -348,15 +349,15 @@ class MetaReferenceInferenceImpl( async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages: list[Message], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -394,13 +395,13 @@ class MetaReferenceInferenceImpl( async def batch_chat_completion( self, model_id: str, - messages_batch: List[List[Message]], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - tools: Optional[List[ToolDefinition]] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages_batch: list[list[Message]], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + tools: list[ToolDefinition] | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> BatchChatCompletionResponse: if sampling_params is None: sampling_params = SamplingParams() @@ -435,15 +436,15 @@ class MetaReferenceInferenceImpl( return BatchChatCompletionResponse(batch=results) async def _nonstream_chat_completion( - self, request_batch: List[ChatCompletionRequest] - ) -> List[ChatCompletionResponse]: + self, request_batch: list[ChatCompletionRequest] + ) -> list[ChatCompletionResponse]: tokenizer = self.generator.formatter.tokenizer first_request = request_batch[0] class ItemState(BaseModel): - tokens: List[int] = [] - logprobs: List[TokenLogProbs] = [] + tokens: list[int] = [] + logprobs: list[TokenLogProbs] = [] stop_reason: StopReason | None = None finished: bool = False @@ -515,7 +516,8 @@ class MetaReferenceInferenceImpl( stop_reason = None ipython = False - for token_result in self.generator.chat_completion(request): + for token_results in self.generator.chat_completion([request]): + token_result = token_results[0] if os.environ.get("LLAMA_MODELS_DEBUG", "0") == "1": cprint(token_result.text, "cyan", end="") if os.environ.get("LLAMA_MODELS_DEBUG", "0") == "2": diff --git a/llama_stack/providers/inline/inference/meta_reference/model_parallel.py b/llama_stack/providers/inline/inference/meta_reference/model_parallel.py index 50640c6d1..9031d36b3 100644 --- a/llama_stack/providers/inline/inference/meta_reference/model_parallel.py +++ b/llama_stack/providers/inline/inference/meta_reference/model_parallel.py @@ -4,9 +4,10 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +from collections.abc import Callable, Generator from copy import deepcopy from functools import partial -from typing import Any, Callable, Generator, List +from typing import Any from llama_stack.models.llama.llama3.chat_format import ChatFormat as Llama3ChatFormat from llama_stack.models.llama.llama4.chat_format import ChatFormat as Llama4ChatFormat @@ -82,7 +83,7 @@ class LlamaModelParallelGenerator: def completion( self, - request_batch: List[CompletionRequestWithRawContent], + request_batch: list[CompletionRequestWithRawContent], ) -> Generator: req_obj = deepcopy(request_batch) gen = self.group.run_inference(("completion", req_obj)) @@ -90,7 +91,7 @@ class LlamaModelParallelGenerator: def chat_completion( self, - request_batch: List[ChatCompletionRequestWithRawContent], + request_batch: list[ChatCompletionRequestWithRawContent], ) -> Generator: req_obj = deepcopy(request_batch) gen = self.group.run_inference(("chat_completion", req_obj)) diff --git a/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py b/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py index 8752f06f3..97e96b929 100644 --- a/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py +++ b/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py @@ -18,8 +18,9 @@ import os import tempfile import time import uuid +from collections.abc import Callable, Generator from enum import Enum -from typing import Callable, Generator, List, Literal, Optional, Tuple, Union +from typing import Annotated, Literal import torch import zmq @@ -30,7 +31,6 @@ from fairscale.nn.model_parallel.initialize import ( ) from pydantic import BaseModel, Field from torch.distributed.launcher.api import LaunchConfig, elastic_launch -from typing_extensions import Annotated from llama_stack.models.llama.datatypes import GenerationResult from llama_stack.providers.utils.inference.prompt_adapter import ( @@ -69,12 +69,15 @@ class CancelSentinel(BaseModel): class TaskRequest(BaseModel): type: Literal[ProcessingMessageName.task_request] = ProcessingMessageName.task_request - task: Tuple[str, List[CompletionRequestWithRawContent] | List[ChatCompletionRequestWithRawContent]] + task: tuple[ + str, + list[CompletionRequestWithRawContent] | list[ChatCompletionRequestWithRawContent], + ] class TaskResponse(BaseModel): type: Literal[ProcessingMessageName.task_response] = ProcessingMessageName.task_response - result: List[GenerationResult] + result: list[GenerationResult] class ExceptionResponse(BaseModel): @@ -82,15 +85,9 @@ class ExceptionResponse(BaseModel): error: str -ProcessingMessage = Union[ - ReadyRequest, - ReadyResponse, - EndSentinel, - CancelSentinel, - TaskRequest, - TaskResponse, - ExceptionResponse, -] +ProcessingMessage = ( + ReadyRequest | ReadyResponse | EndSentinel | CancelSentinel | TaskRequest | TaskResponse | ExceptionResponse +) class ProcessingMessageWrapper(BaseModel): @@ -200,7 +197,7 @@ def maybe_get_work(sock: zmq.Socket): return client_id, message -def maybe_parse_message(maybe_json: Optional[str]) -> Optional[ProcessingMessage]: +def maybe_parse_message(maybe_json: str | None) -> ProcessingMessage | None: if maybe_json is None: return None try: @@ -231,10 +228,10 @@ def worker_process_entrypoint( while True: try: task = req_gen.send(result) - if isinstance(task, str) and task == EndSentinel(): + if isinstance(task, EndSentinel): break - assert isinstance(task, TaskRequest) + assert isinstance(task, TaskRequest), task result = model(task.task) except StopIteration: break @@ -331,7 +328,10 @@ class ModelParallelProcessGroup: def run_inference( self, - req: Tuple[str, List[CompletionRequestWithRawContent] | List[ChatCompletionRequestWithRawContent]], + req: tuple[ + str, + list[CompletionRequestWithRawContent] | list[ChatCompletionRequestWithRawContent], + ], ) -> Generator: assert not self.running, "inference already running" diff --git a/llama_stack/providers/inline/inference/sentence_transformers/__init__.py b/llama_stack/providers/inline/inference/sentence_transformers/__init__.py index c1d65d10c..1719cbacc 100644 --- a/llama_stack/providers/inline/inference/sentence_transformers/__init__.py +++ b/llama_stack/providers/inline/inference/sentence_transformers/__init__.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.providers.inline.inference.sentence_transformers.config import ( SentenceTransformersInferenceConfig, @@ -13,7 +13,7 @@ from llama_stack.providers.inline.inference.sentence_transformers.config import async def get_provider_impl( config: SentenceTransformersInferenceConfig, - _deps: Dict[str, Any], + _deps: dict[str, Any], ): from .sentence_transformers import SentenceTransformersInferenceImpl diff --git a/llama_stack/providers/inline/inference/sentence_transformers/config.py b/llama_stack/providers/inline/inference/sentence_transformers/config.py index 93e0afe11..b03010b10 100644 --- a/llama_stack/providers/inline/inference/sentence_transformers/config.py +++ b/llama_stack/providers/inline/inference/sentence_transformers/config.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel class SentenceTransformersInferenceConfig(BaseModel): @classmethod - def sample_run_config(cls, **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs) -> dict[str, Any]: return {} diff --git a/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py b/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py index d717d055f..7b36b0997 100644 --- a/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py +++ b/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import logging -from typing import AsyncGenerator, List, Optional, Union +from collections.abc import AsyncGenerator from llama_stack.apis.inference import ( CompletionResponse, @@ -60,46 +60,46 @@ class SentenceTransformersInferenceImpl( self, model_id: str, content: str, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - ) -> Union[CompletionResponse, AsyncGenerator]: + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + ) -> CompletionResponse | AsyncGenerator: raise ValueError("Sentence transformers don't support completion") async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages: list[Message], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> AsyncGenerator: raise ValueError("Sentence transformers don't support chat completion") async def batch_completion( self, model_id: str, - content_batch: List[InterleavedContent], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + content_batch: list[InterleavedContent], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ): raise NotImplementedError("Batch completion is not supported for Sentence Transformers") async def batch_chat_completion( self, model_id: str, - messages_batch: List[List[Message]], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_config: Optional[ToolConfig] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + messages_batch: list[list[Message]], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_config: ToolConfig | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ): raise NotImplementedError("Batch chat completion is not supported for Sentence Transformers") diff --git a/llama_stack/providers/inline/inference/vllm/__init__.py b/llama_stack/providers/inline/inference/vllm/__init__.py index bd0551e57..d0ec3e084 100644 --- a/llama_stack/providers/inline/inference/vllm/__init__.py +++ b/llama_stack/providers/inline/inference/vllm/__init__.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from .config import VLLMConfig -async def get_provider_impl(config: VLLMConfig, _deps: Dict[str, Any]): +async def get_provider_impl(config: VLLMConfig, _deps: dict[str, Any]): from .vllm import VLLMInferenceImpl impl = VLLMInferenceImpl(config) diff --git a/llama_stack/providers/inline/inference/vllm/config.py b/llama_stack/providers/inline/inference/vllm/config.py index 51d48e6d5..ce8743c74 100644 --- a/llama_stack/providers/inline/inference/vllm/config.py +++ b/llama_stack/providers/inline/inference/vllm/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel, Field @@ -42,7 +42,7 @@ class VLLMConfig(BaseModel): ) @classmethod - def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs: Any) -> dict[str, Any]: return { "tensor_parallel_size": "${env.TENSOR_PARALLEL_SIZE:1}", "max_tokens": "${env.MAX_TOKENS:4096}", diff --git a/llama_stack/providers/inline/inference/vllm/openai_utils.py b/llama_stack/providers/inline/inference/vllm/openai_utils.py index d34f5ad5f..77cbf0403 100644 --- a/llama_stack/providers/inline/inference/vllm/openai_utils.py +++ b/llama_stack/providers/inline/inference/vllm/openai_utils.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List, Optional import vllm @@ -55,8 +54,8 @@ def _merge_context_into_content(message: Message) -> Message: # type: ignore def _llama_stack_tools_to_openai_tools( - tools: Optional[List[ToolDefinition]] = None, -) -> List[vllm.entrypoints.openai.protocol.ChatCompletionToolsParam]: + tools: list[ToolDefinition] | None = None, +) -> list[vllm.entrypoints.openai.protocol.ChatCompletionToolsParam]: """ Convert the list of available tools from Llama Stack's format to vLLM's version of OpenAI's format. diff --git a/llama_stack/providers/inline/inference/vllm/vllm.py b/llama_stack/providers/inline/inference/vllm/vllm.py index 9d742c39c..438cb14a0 100644 --- a/llama_stack/providers/inline/inference/vllm/vllm.py +++ b/llama_stack/providers/inline/inference/vllm/vllm.py @@ -7,7 +7,7 @@ import json import re import uuid -from typing import AsyncGenerator, AsyncIterator, Dict, List, Optional, Union +from collections.abc import AsyncGenerator, AsyncIterator # These vLLM modules contain names that overlap with Llama Stack names, so we import # fully-qualified names @@ -100,7 +100,7 @@ def _random_uuid_str() -> str: def _response_format_to_guided_decoding_params( - response_format: Optional[ResponseFormat], # type: ignore + response_format: ResponseFormat | None, # type: ignore ) -> vllm.sampling_params.GuidedDecodingParams: """ Translate constrained decoding parameters from Llama Stack's format to vLLM's format. @@ -131,9 +131,9 @@ def _response_format_to_guided_decoding_params( def _convert_sampling_params( - sampling_params: Optional[SamplingParams], - response_format: Optional[ResponseFormat], # type: ignore - log_prob_config: Optional[LogProbConfig], + sampling_params: SamplingParams | None, + response_format: ResponseFormat | None, # type: ignore + log_prob_config: LogProbConfig | None, ) -> vllm.SamplingParams: """Convert sampling and constrained decoding configuration from Llama Stack's format to vLLM's format.""" @@ -370,11 +370,11 @@ class VLLMInferenceImpl( self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - ) -> Union[CompletionResponse, AsyncIterator[CompletionResponseStreamChunk]]: + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]: if model_id not in self.model_ids: raise ValueError( f"This adapter is not registered to model id '{model_id}'. Registered IDs are: {self.model_ids}" @@ -403,25 +403,25 @@ class VLLMInferenceImpl( async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: raise NotImplementedError() async def chat_completion( self, model_id: str, - messages: List[Message], # type: ignore - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, # type: ignore - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages: list[Message], # type: ignore + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, # type: ignore + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> ChatCompletionResponse | ChatCompletionResponseStreamChunk: sampling_params = sampling_params or SamplingParams() if model_id not in self.model_ids: @@ -605,7 +605,7 @@ class VLLMInferenceImpl( async def _chat_completion_for_meta_llama( self, request: ChatCompletionRequest - ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]: + ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]: """ Subroutine that routes chat completions for Meta Llama models through Llama Stack's chat template instead of using vLLM's version of that template. The Llama Stack version @@ -701,7 +701,7 @@ class VLLMInferenceImpl( # Tool calls come in pieces, but Llama Stack expects them in bigger chunks. We build up # those chunks and output them at the end. # This data structure holds the current set of partial tool calls. - index_to_tool_call: Dict[int, Dict] = dict() + index_to_tool_call: dict[int, dict] = dict() # The Llama Stack event stream must always start with a start event. Use an empty one to # simplify logic below diff --git a/llama_stack/providers/inline/post_training/common/validator.py b/llama_stack/providers/inline/post_training/common/validator.py index b0aec6187..950b75f86 100644 --- a/llama_stack/providers/inline/post_training/common/validator.py +++ b/llama_stack/providers/inline/post_training/common/validator.py @@ -17,10 +17,8 @@ from llama_stack.apis.common.type_system import ( DialogType, StringType, ) -from llama_stack.apis.datasets import Datasets from llama_stack.providers.utils.common.data_schema_validator import ( ColumnName, - validate_dataset_schema, ) EXPECTED_DATASET_SCHEMA: dict[str, list[dict[str, Any]]] = { @@ -36,21 +34,3 @@ EXPECTED_DATASET_SCHEMA: dict[str, list[dict[str, Any]]] = { } ], } - - -async def validate_input_dataset_schema( - datasets_api: Datasets, - dataset_id: str, - dataset_type: str, -) -> None: - dataset_def = await datasets_api.get_dataset(dataset_id=dataset_id) - if not dataset_def: - raise ValueError(f"Dataset {dataset_id} does not exist.") - - if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0: - raise ValueError(f"Dataset {dataset_id} does not have a schema defined.") - - if dataset_type not in EXPECTED_DATASET_SCHEMA: - raise ValueError(f"Dataset type {dataset_type} is not supported.") - - validate_dataset_schema(dataset_def.dataset_schema, EXPECTED_DATASET_SCHEMA[dataset_type]) diff --git a/llama_stack/providers/inline/post_training/torchtune/__init__.py b/llama_stack/providers/inline/post_training/torchtune/__init__.py index ca7801be7..7a2f9eba2 100644 --- a/llama_stack/providers/inline/post_training/torchtune/__init__.py +++ b/llama_stack/providers/inline/post_training/torchtune/__init__.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.distribution.datatypes import Api @@ -15,7 +15,7 @@ from .config import TorchtunePostTrainingConfig async def get_provider_impl( config: TorchtunePostTrainingConfig, - deps: Dict[Api, Any], + deps: dict[Api, Any], ): from .post_training import TorchtunePostTrainingImpl diff --git a/llama_stack/providers/inline/post_training/torchtune/common/checkpointer.py b/llama_stack/providers/inline/post_training/torchtune/common/checkpointer.py index fcadd0884..af8bd2765 100644 --- a/llama_stack/providers/inline/post_training/torchtune/common/checkpointer.py +++ b/llama_stack/providers/inline/post_training/torchtune/common/checkpointer.py @@ -8,7 +8,7 @@ import json import os import shutil from pathlib import Path -from typing import Any, Dict, List +from typing import Any import torch from safetensors.torch import save_file @@ -34,7 +34,7 @@ class TorchtuneCheckpointer: model_id: str, training_algorithm: str, checkpoint_dir: str, - checkpoint_files: List[str], + checkpoint_files: list[str], output_dir: str, model_type: str, ): @@ -54,11 +54,11 @@ class TorchtuneCheckpointer: # get ckpt paths self._checkpoint_path = Path.joinpath(self._checkpoint_dir, self._checkpoint_file) - def load_checkpoint(self) -> Dict[str, Any]: + def load_checkpoint(self) -> dict[str, Any]: """ Load Meta checkpoint from file. Currently only loading from a single file is supported. """ - state_dict: Dict[str, Any] = {} + state_dict: dict[str, Any] = {} model_state_dict = safe_torch_load(self._checkpoint_path) if self._model_type == ModelType.LLAMA3_VISION: from torchtune.models.llama3_2_vision._convert_weights import ( @@ -82,7 +82,7 @@ class TorchtuneCheckpointer: def save_checkpoint( self, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], epoch: int, adapter_only: bool = False, checkpoint_format: str | None = None, @@ -100,7 +100,7 @@ class TorchtuneCheckpointer: def _save_meta_format_checkpoint( self, model_file_path: Path, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], adapter_only: bool = False, ) -> None: model_file_path.mkdir(parents=True, exist_ok=True) @@ -168,7 +168,7 @@ class TorchtuneCheckpointer: def _save_hf_format_checkpoint( self, model_file_path: Path, - state_dict: Dict[str, Any], + state_dict: dict[str, Any], ) -> None: # the config.json file contains model params needed for state dict conversion config = json.loads(Path.joinpath(self._checkpoint_dir.parent, "config.json").read_text()) @@ -179,7 +179,7 @@ class TorchtuneCheckpointer: repo_id_path = Path.joinpath(self._checkpoint_dir.parent, REPO_ID_FNAME).with_suffix(".json") self.repo_id = None if repo_id_path.exists(): - with open(repo_id_path, "r") as json_file: + with open(repo_id_path) as json_file: data = json.load(json_file) self.repo_id = data.get("repo_id") diff --git a/llama_stack/providers/inline/post_training/torchtune/common/utils.py b/llama_stack/providers/inline/post_training/torchtune/common/utils.py index a040ca1b0..f0fa052a2 100644 --- a/llama_stack/providers/inline/post_training/torchtune/common/utils.py +++ b/llama_stack/providers/inline/post_training/torchtune/common/utils.py @@ -10,7 +10,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Callable, Dict +from collections.abc import Callable import torch from pydantic import BaseModel @@ -35,7 +35,7 @@ class ModelConfig(BaseModel): checkpoint_type: str -MODEL_CONFIGS: Dict[str, ModelConfig] = { +MODEL_CONFIGS: dict[str, ModelConfig] = { "Llama3.2-3B-Instruct": ModelConfig( model_definition=lora_llama3_2_3b, tokenizer_type=llama3_tokenizer, @@ -48,7 +48,7 @@ MODEL_CONFIGS: Dict[str, ModelConfig] = { ), } -DATA_FORMATS: Dict[str, Transform] = { +DATA_FORMATS: dict[str, Transform] = { "instruct": InputOutputToMessages, "dialog": ShareGPTToMessages, } diff --git a/llama_stack/providers/inline/post_training/torchtune/config.py b/llama_stack/providers/inline/post_training/torchtune/config.py index ee3504f9e..f3ce874aa 100644 --- a/llama_stack/providers/inline/post_training/torchtune/config.py +++ b/llama_stack/providers/inline/post_training/torchtune/config.py @@ -4,17 +4,17 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Literal, Optional +from typing import Any, Literal from pydantic import BaseModel class TorchtunePostTrainingConfig(BaseModel): - torch_seed: Optional[int] = None - checkpoint_format: Optional[Literal["meta", "huggingface"]] = "meta" + torch_seed: int | None = None + checkpoint_format: Literal["meta", "huggingface"] | None = "meta" @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return { "checkpoint_format": "meta", } diff --git a/llama_stack/providers/inline/post_training/torchtune/datasets/format_adapter.py b/llama_stack/providers/inline/post_training/torchtune/datasets/format_adapter.py index 6b607f1c7..96dd8b8dd 100644 --- a/llama_stack/providers/inline/post_training/torchtune/datasets/format_adapter.py +++ b/llama_stack/providers/inline/post_training/torchtune/datasets/format_adapter.py @@ -11,7 +11,8 @@ # LICENSE file in the root directory of this source tree. import json -from typing import Any, Mapping +from collections.abc import Mapping +from typing import Any from llama_stack.providers.utils.common.data_schema_validator import ColumnName diff --git a/llama_stack/providers/inline/post_training/torchtune/datasets/sft.py b/llama_stack/providers/inline/post_training/torchtune/datasets/sft.py index 050996860..ae7faf31e 100644 --- a/llama_stack/providers/inline/post_training/torchtune/datasets/sft.py +++ b/llama_stack/providers/inline/post_training/torchtune/datasets/sft.py @@ -10,7 +10,8 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -from typing import Any, Dict, List, Mapping +from collections.abc import Mapping +from typing import Any import numpy as np from torch.utils.data import Dataset @@ -27,7 +28,7 @@ from llama_stack.providers.inline.post_training.torchtune.datasets.format_adapte class SFTDataset(Dataset): def __init__( self, - rows: List[Dict[str, Any]], + rows: list[dict[str, Any]], message_transform: Transform, model_transform: Transform, dataset_type: str, @@ -40,11 +41,11 @@ class SFTDataset(Dataset): def __len__(self): return len(self._rows) - def __getitem__(self, index: int) -> Dict[str, Any]: + def __getitem__(self, index: int) -> dict[str, Any]: sample = self._rows[index] return self._prepare_sample(sample) - def _prepare_sample(self, sample: Mapping[str, Any]) -> Dict[str, Any]: + def _prepare_sample(self, sample: Mapping[str, Any]) -> dict[str, Any]: if self._dataset_type == "instruct": sample = llama_stack_instruct_to_torchtune_instruct(sample) elif self._dataset_type == "dialog": diff --git a/llama_stack/providers/inline/post_training/torchtune/post_training.py b/llama_stack/providers/inline/post_training/torchtune/post_training.py index cc1a6a5fe..c7d8d6758 100644 --- a/llama_stack/providers/inline/post_training/torchtune/post_training.py +++ b/llama_stack/providers/inline/post_training/torchtune/post_training.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. from enum import Enum -from typing import Any, Dict, Optional +from typing import Any from llama_stack.apis.datasetio import DatasetIO from llama_stack.apis.datasets import Datasets @@ -64,7 +64,7 @@ class TorchtunePostTrainingImpl: ) @staticmethod - def _resources_stats_to_artifact(resources_stats: Dict[str, Any]) -> JobArtifact: + def _resources_stats_to_artifact(resources_stats: dict[str, Any]) -> JobArtifact: return JobArtifact( type=TrainingArtifactType.RESOURCES_STATS.value, name=TrainingArtifactType.RESOURCES_STATS.value, @@ -75,11 +75,11 @@ class TorchtunePostTrainingImpl: self, job_uuid: str, training_config: TrainingConfig, - hyperparam_search_config: Dict[str, Any], - logger_config: Dict[str, Any], + hyperparam_search_config: dict[str, Any], + logger_config: dict[str, Any], model: str, - checkpoint_dir: Optional[str], - algorithm_config: Optional[AlgorithmConfig], + checkpoint_dir: str | None, + algorithm_config: AlgorithmConfig | None, ) -> PostTrainingJob: if isinstance(algorithm_config, LoraFinetuningConfig): @@ -121,8 +121,8 @@ class TorchtunePostTrainingImpl: finetuned_model: str, algorithm_config: DPOAlignmentConfig, training_config: TrainingConfig, - hyperparam_search_config: Dict[str, Any], - logger_config: Dict[str, Any], + hyperparam_search_config: dict[str, Any], + logger_config: dict[str, Any], ) -> PostTrainingJob: ... async def get_training_jobs(self) -> ListPostTrainingJobsResponse: @@ -144,7 +144,7 @@ class TorchtunePostTrainingImpl: return data[0] if data else None @webmethod(route="/post-training/job/status") - async def get_training_job_status(self, job_uuid: str) -> Optional[PostTrainingJobStatusResponse]: + async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse | None: job = self._scheduler.get_job(job_uuid) match job.status: @@ -175,6 +175,6 @@ class TorchtunePostTrainingImpl: self._scheduler.cancel(job_uuid) @webmethod(route="/post-training/job/artifacts") - async def get_training_job_artifacts(self, job_uuid: str) -> Optional[PostTrainingJobArtifactsResponse]: + async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse | None: job = self._scheduler.get_job(job_uuid) return PostTrainingJobArtifactsResponse(job_uuid=job_uuid, checkpoints=self._get_checkpoints(job)) diff --git a/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py b/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py index 04bf86b97..1239523cd 100644 --- a/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py +++ b/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py @@ -11,7 +11,7 @@ import time from datetime import datetime, timezone from functools import partial from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple +from typing import Any import torch from torch import nn @@ -48,9 +48,6 @@ from llama_stack.apis.post_training import ( from llama_stack.distribution.utils.config_dirs import DEFAULT_CHECKPOINT_DIR from llama_stack.distribution.utils.model_utils import model_local_dir from llama_stack.models.llama.sku_list import resolve_model -from llama_stack.providers.inline.post_training.common.validator import ( - validate_input_dataset_schema, -) from llama_stack.providers.inline.post_training.torchtune.common import utils from llama_stack.providers.inline.post_training.torchtune.common.checkpointer import ( TorchtuneCheckpointer, @@ -83,10 +80,10 @@ class LoraFinetuningSingleDevice: config: TorchtunePostTrainingConfig, job_uuid: str, training_config: TrainingConfig, - hyperparam_search_config: Dict[str, Any], - logger_config: Dict[str, Any], + hyperparam_search_config: dict[str, Any], + logger_config: dict[str, Any], model: str, - checkpoint_dir: Optional[str], + checkpoint_dir: str | None, algorithm_config: LoraFinetuningConfig | QATFinetuningConfig | None, datasetio_api: DatasetIO, datasets_api: Datasets, @@ -159,7 +156,7 @@ class LoraFinetuningSingleDevice: self.datasets_api = datasets_api async def load_checkpoint(self): - def get_checkpoint_files(checkpoint_dir: str) -> List[str]: + def get_checkpoint_files(checkpoint_dir: str) -> list[str]: try: # List all files in the given directory files = os.listdir(checkpoint_dir) @@ -253,8 +250,8 @@ class LoraFinetuningSingleDevice: self, enable_activation_checkpointing: bool, enable_activation_offloading: bool, - base_model_state_dict: Dict[str, Any], - lora_weights_state_dict: Optional[Dict[str, Any]] = None, + base_model_state_dict: dict[str, Any], + lora_weights_state_dict: dict[str, Any] | None = None, ) -> nn.Module: self._lora_rank = self.algorithm_config.rank self._lora_alpha = self.algorithm_config.alpha @@ -338,7 +335,7 @@ class LoraFinetuningSingleDevice: tokenizer: Llama3Tokenizer, shuffle: bool, batch_size: int, - ) -> Tuple[DistributedSampler, DataLoader]: + ) -> tuple[DistributedSampler, DataLoader]: async def fetch_rows(dataset_id: str): return await self.datasetio_api.iterrows( dataset_id=dataset_id, @@ -348,11 +345,9 @@ class LoraFinetuningSingleDevice: all_rows = await fetch_rows(dataset_id) rows = all_rows.data - await validate_input_dataset_schema( - datasets_api=self.datasets_api, - dataset_id=dataset_id, - dataset_type=self._data_format.value, - ) + # TODO (xiyan): validate dataset schema + # dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id) + data_transform = await utils.get_data_transform(self._data_format) ds = SFTDataset( rows, @@ -435,7 +430,7 @@ class LoraFinetuningSingleDevice: checkpoint_format=self._checkpoint_format, ) - async def _loss_step(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor: + async def _loss_step(self, batch: dict[str, torch.Tensor]) -> torch.Tensor: # Shape [b, s], needed for the loss not the model labels = batch.pop("labels") # run model @@ -457,7 +452,7 @@ class LoraFinetuningSingleDevice: return loss - async def train(self) -> Tuple[Dict[str, Any], List[Checkpoint]]: + async def train(self) -> tuple[dict[str, Any], list[Checkpoint]]: """ The core training loop. """ @@ -469,7 +464,7 @@ class LoraFinetuningSingleDevice: # training artifacts checkpoints = [] - memory_stats: Dict[str, Any] = {} + memory_stats: dict[str, Any] = {} # self.epochs_run should be non-zero when we're resuming from a checkpoint for curr_epoch in range(self.epochs_run, self.total_epochs): @@ -570,7 +565,7 @@ class LoraFinetuningSingleDevice: return (memory_stats, checkpoints) - async def validation(self) -> Tuple[float, float]: + async def validation(self) -> tuple[float, float]: total_loss = 0.0 total_tokens = 0 log.info("Starting validation...") diff --git a/llama_stack/providers/inline/safety/code_scanner/__init__.py b/llama_stack/providers/inline/safety/code_scanner/__init__.py index 62975a963..68e32b747 100644 --- a/llama_stack/providers/inline/safety/code_scanner/__init__.py +++ b/llama_stack/providers/inline/safety/code_scanner/__init__.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from .config import CodeScannerConfig -async def get_provider_impl(config: CodeScannerConfig, deps: Dict[str, Any]): +async def get_provider_impl(config: CodeScannerConfig, deps: dict[str, Any]): from .code_scanner import MetaReferenceCodeScannerSafetyImpl impl = MetaReferenceCodeScannerSafetyImpl(config, deps) diff --git a/llama_stack/providers/inline/safety/code_scanner/code_scanner.py b/llama_stack/providers/inline/safety/code_scanner/code_scanner.py index 606d11d2c..be05ee436 100644 --- a/llama_stack/providers/inline/safety/code_scanner/code_scanner.py +++ b/llama_stack/providers/inline/safety/code_scanner/code_scanner.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import logging -from typing import Any, Dict, List +from typing import Any from llama_stack.apis.inference import Message from llama_stack.apis.safety import ( @@ -48,8 +48,8 @@ class MetaReferenceCodeScannerSafetyImpl(Safety): async def run_shield( self, shield_id: str, - messages: List[Message], - params: Dict[str, Any] = None, + messages: list[Message], + params: dict[str, Any] = None, ) -> RunShieldResponse: shield = await self.shield_store.get_shield(shield_id) if not shield: diff --git a/llama_stack/providers/inline/safety/code_scanner/config.py b/llama_stack/providers/inline/safety/code_scanner/config.py index 1d880ee9c..66eb8e368 100644 --- a/llama_stack/providers/inline/safety/code_scanner/config.py +++ b/llama_stack/providers/inline/safety/code_scanner/config.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel class CodeScannerConfig(BaseModel): @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return {} diff --git a/llama_stack/providers/inline/safety/llama_guard/__init__.py b/llama_stack/providers/inline/safety/llama_guard/__init__.py index a4263b169..8865cc344 100644 --- a/llama_stack/providers/inline/safety/llama_guard/__init__.py +++ b/llama_stack/providers/inline/safety/llama_guard/__init__.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from .config import LlamaGuardConfig -async def get_provider_impl(config: LlamaGuardConfig, deps: Dict[str, Any]): +async def get_provider_impl(config: LlamaGuardConfig, deps: dict[str, Any]): from .llama_guard import LlamaGuardSafetyImpl assert isinstance(config, LlamaGuardConfig), f"Unexpected config type: {type(config)}" diff --git a/llama_stack/providers/inline/safety/llama_guard/config.py b/llama_stack/providers/inline/safety/llama_guard/config.py index 53849ab33..412e7218d 100644 --- a/llama_stack/providers/inline/safety/llama_guard/config.py +++ b/llama_stack/providers/inline/safety/llama_guard/config.py @@ -4,16 +4,16 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List +from typing import Any from pydantic import BaseModel class LlamaGuardConfig(BaseModel): - excluded_categories: List[str] = [] + excluded_categories: list[str] = [] @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return { "excluded_categories": [], } diff --git a/llama_stack/providers/inline/safety/llama_guard/llama_guard.py b/llama_stack/providers/inline/safety/llama_guard/llama_guard.py index 2ab16f986..937301c2e 100644 --- a/llama_stack/providers/inline/safety/llama_guard/llama_guard.py +++ b/llama_stack/providers/inline/safety/llama_guard/llama_guard.py @@ -6,7 +6,7 @@ import re from string import Template -from typing import Any, Dict, List, Optional +from typing import Any from llama_stack.apis.common.content_types import ImageContentItem, TextContentItem from llama_stack.apis.inference import ( @@ -149,8 +149,8 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate): async def run_shield( self, shield_id: str, - messages: List[Message], - params: Dict[str, Any] = None, + messages: list[Message], + params: dict[str, Any] = None, ) -> RunShieldResponse: shield = await self.shield_store.get_shield(shield_id) if not shield: @@ -177,7 +177,7 @@ class LlamaGuardShield: self, model: str, inference_api: Inference, - excluded_categories: Optional[List[str]] = None, + excluded_categories: list[str] | None = None, ): if excluded_categories is None: excluded_categories = [] @@ -193,7 +193,7 @@ class LlamaGuardShield: self.inference_api = inference_api self.excluded_categories = excluded_categories - def check_unsafe_response(self, response: str) -> Optional[str]: + def check_unsafe_response(self, response: str) -> str | None: match = re.match(r"^unsafe\n(.*)$", response) if match: # extracts the unsafe code @@ -202,7 +202,7 @@ class LlamaGuardShield: return None - def get_safety_categories(self) -> List[str]: + def get_safety_categories(self) -> list[str]: excluded_categories = self.excluded_categories if set(excluded_categories) == set(SAFETY_CATEGORIES_TO_CODE_MAP.values()): excluded_categories = [] @@ -218,7 +218,7 @@ class LlamaGuardShield: return final_categories - def validate_messages(self, messages: List[Message]) -> None: + def validate_messages(self, messages: list[Message]) -> None: if len(messages) == 0: raise ValueError("Messages must not be empty") if messages[0].role != Role.user.value: @@ -229,7 +229,7 @@ class LlamaGuardShield: return messages - async def run(self, messages: List[Message]) -> RunShieldResponse: + async def run(self, messages: list[Message]) -> RunShieldResponse: messages = self.validate_messages(messages) if self.model == CoreModelId.llama_guard_3_11b_vision.value: @@ -247,10 +247,10 @@ class LlamaGuardShield: content = content.strip() return self.get_shield_response(content) - def build_text_shield_input(self, messages: List[Message]) -> UserMessage: + def build_text_shield_input(self, messages: list[Message]) -> UserMessage: return UserMessage(content=self.build_prompt(messages)) - def build_vision_shield_input(self, messages: List[Message]) -> UserMessage: + def build_vision_shield_input(self, messages: list[Message]) -> UserMessage: conversation = [] most_recent_img = None @@ -284,7 +284,7 @@ class LlamaGuardShield: return UserMessage(content=prompt) - def build_prompt(self, messages: List[Message]) -> str: + def build_prompt(self, messages: list[Message]) -> str: categories = self.get_safety_categories() categories_str = "\n".join(categories) conversations_str = "\n\n".join( diff --git a/llama_stack/providers/inline/safety/prompt_guard/__init__.py b/llama_stack/providers/inline/safety/prompt_guard/__init__.py index 747f34421..1761c9138 100644 --- a/llama_stack/providers/inline/safety/prompt_guard/__init__.py +++ b/llama_stack/providers/inline/safety/prompt_guard/__init__.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any -from .config import PromptGuardConfig # noqa: F401 +from .config import PromptGuardConfig -async def get_provider_impl(config: PromptGuardConfig, deps: Dict[str, Any]): +async def get_provider_impl(config: PromptGuardConfig, deps: dict[str, Any]): from .prompt_guard import PromptGuardSafetyImpl impl = PromptGuardSafetyImpl(config, deps) diff --git a/llama_stack/providers/inline/safety/prompt_guard/config.py b/llama_stack/providers/inline/safety/prompt_guard/config.py index 76bd5978d..69ea512c5 100644 --- a/llama_stack/providers/inline/safety/prompt_guard/config.py +++ b/llama_stack/providers/inline/safety/prompt_guard/config.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Any, Dict +from typing import Any from pydantic import BaseModel, field_validator @@ -26,7 +26,7 @@ class PromptGuardConfig(BaseModel): return v @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return { "guard_type": "injection", } diff --git a/llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py b/llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py index fce3e3d14..56ce8285f 100644 --- a/llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py +++ b/llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import logging -from typing import Any, Dict, List +from typing import Any import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer @@ -49,8 +49,8 @@ class PromptGuardSafetyImpl(Safety, ShieldsProtocolPrivate): async def run_shield( self, shield_id: str, - messages: List[Message], - params: Dict[str, Any] = None, + messages: list[Message], + params: dict[str, Any] = None, ) -> RunShieldResponse: shield = await self.shield_store.get_shield(shield_id) if not shield: @@ -81,7 +81,7 @@ class PromptGuardShield: self.tokenizer = AutoTokenizer.from_pretrained(model_dir) self.model = AutoModelForSequenceClassification.from_pretrained(model_dir, device_map=self.device) - async def run(self, messages: List[Message]) -> RunShieldResponse: + async def run(self, messages: list[Message]) -> RunShieldResponse: message = messages[-1] text = interleaved_content_as_str(message.content) diff --git a/llama_stack/providers/inline/scoring/basic/__init__.py b/llama_stack/providers/inline/scoring/basic/__init__.py index 4898b973a..d9d150b1a 100644 --- a/llama_stack/providers/inline/scoring/basic/__init__.py +++ b/llama_stack/providers/inline/scoring/basic/__init__.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.distribution.datatypes import Api @@ -12,7 +12,7 @@ from .config import BasicScoringConfig async def get_provider_impl( config: BasicScoringConfig, - deps: Dict[Api, Any], + deps: dict[Api, Any], ): from .scoring import BasicScoringImpl diff --git a/llama_stack/providers/inline/scoring/basic/config.py b/llama_stack/providers/inline/scoring/basic/config.py index 5866be359..e9c7fb451 100644 --- a/llama_stack/providers/inline/scoring/basic/config.py +++ b/llama_stack/providers/inline/scoring/basic/config.py @@ -3,12 +3,12 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel class BasicScoringConfig(BaseModel): @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return {} diff --git a/llama_stack/providers/inline/scoring/basic/scoring.py b/llama_stack/providers/inline/scoring/basic/scoring.py index 9a45f7139..09f89be5e 100644 --- a/llama_stack/providers/inline/scoring/basic/scoring.py +++ b/llama_stack/providers/inline/scoring/basic/scoring.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Optional +from typing import Any from llama_stack.apis.datasetio import DatasetIO from llama_stack.apis.datasets import Datasets @@ -66,7 +66,7 @@ class BasicScoringImpl( async def shutdown(self) -> None: ... - async def list_scoring_functions(self) -> List[ScoringFn]: + async def list_scoring_functions(self) -> list[ScoringFn]: scoring_fn_defs_list = [ fn_def for impl in self.scoring_fn_id_impls.values() for fn_def in impl.get_supported_scoring_fn_defs() ] @@ -82,7 +82,7 @@ class BasicScoringImpl( async def score_batch( self, dataset_id: str, - scoring_functions: Dict[str, Optional[ScoringFnParams]] = None, + scoring_functions: dict[str, ScoringFnParams | None] = None, save_results_dataset: bool = False, ) -> ScoreBatchResponse: dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id) @@ -107,8 +107,8 @@ class BasicScoringImpl( async def score( self, - input_rows: List[Dict[str, Any]], - scoring_functions: Dict[str, Optional[ScoringFnParams]] = None, + input_rows: list[dict[str, Any]], + scoring_functions: dict[str, ScoringFnParams | None] = None, ) -> ScoreResponse: res = {} for scoring_fn_id in scoring_functions.keys(): diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py index f37780f3e..b29620be2 100644 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py +++ b/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py @@ -6,7 +6,7 @@ import json import re -from typing import Any, Dict, Optional +from typing import Any from llama_stack.apis.scoring import ScoringResultRow from llama_stack.apis.scoring_functions import ScoringFnParams @@ -17,7 +17,7 @@ from ..utils.bfcl.checker import ast_checker, is_empty_output from .fn_defs.bfcl import bfcl -def postprocess(x: Dict[str, Any], test_category: str) -> Dict[str, Any]: +def postprocess(x: dict[str, Any], test_category: str) -> dict[str, Any]: contain_func_call = False error = None error_type = None @@ -52,11 +52,11 @@ def postprocess(x: Dict[str, Any], test_category: str) -> Dict[str, Any]: } -def gen_valid(x: Dict[str, Any]) -> Dict[str, float]: +def gen_valid(x: dict[str, Any]) -> dict[str, float]: return {"valid": x["valid"]} -def gen_relevance_acc(x: Dict[str, Any]) -> Dict[str, float]: +def gen_relevance_acc(x: dict[str, Any]) -> dict[str, float]: # This function serves for both relevance and irrelevance tests, which share the exact opposite logic. # If `test_category` is "irrelevance", the model is expected to output no function call. # No function call means either the AST decoding fails (a error message is generated) or the decoded AST does not contain any function call (such as a empty list, `[]`). @@ -78,9 +78,9 @@ class BFCLScoringFn(RegisteredBaseScoringFn): async def score_row( self, - input_row: Dict[str, Any], - scoring_fn_identifier: Optional[str] = "bfcl", - scoring_params: Optional[ScoringFnParams] = None, + input_row: dict[str, Any], + scoring_fn_identifier: str | None = "bfcl", + scoring_params: ScoringFnParams | None = None, ) -> ScoringResultRow: test_category = re.sub(r"_[0-9_-]+$", "", input_row["id"]) score_result = postprocess(input_row, test_category) diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/docvqa_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/docvqa_scoring_fn.py index 84ca55732..b87974d08 100644 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/docvqa_scoring_fn.py +++ b/llama_stack/providers/inline/scoring/basic/scoring_fn/docvqa_scoring_fn.py @@ -6,7 +6,7 @@ import json import re -from typing import Any, Dict, Optional +from typing import Any from llama_stack.apis.scoring import ScoringResultRow from llama_stack.apis.scoring_functions import ScoringFnParams @@ -228,9 +228,9 @@ class DocVQAScoringFn(RegisteredBaseScoringFn): async def score_row( self, - input_row: Dict[str, Any], - scoring_fn_identifier: Optional[str] = "docvqa", - scoring_params: Optional[ScoringFnParams] = None, + input_row: dict[str, Any], + scoring_fn_identifier: str | None = "docvqa", + scoring_params: ScoringFnParams | None = None, ) -> ScoringResultRow: expected_answers = json.loads(input_row["expected_answer"]) generated_answer = input_row["generated_answer"] diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/equality_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/equality_scoring_fn.py index 0bd6bdd48..60804330f 100644 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/equality_scoring_fn.py +++ b/llama_stack/providers/inline/scoring/basic/scoring_fn/equality_scoring_fn.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from llama_stack.apis.scoring import ScoringResultRow from llama_stack.apis.scoring_functions import ScoringFnParams @@ -26,9 +26,9 @@ class EqualityScoringFn(RegisteredBaseScoringFn): async def score_row( self, - input_row: Dict[str, Any], - scoring_fn_identifier: Optional[str] = "equality", - scoring_params: Optional[ScoringFnParams] = None, + input_row: dict[str, Any], + scoring_fn_identifier: str | None = "equality", + scoring_params: ScoringFnParams | None = None, ) -> ScoringResultRow: assert "expected_answer" in input_row, "Expected answer not found in input row." assert "generated_answer" in input_row, "Generated answer not found in input row." diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/ifeval_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/ifeval_scoring_fn.py index 6ff856684..77f6176e6 100644 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/ifeval_scoring_fn.py +++ b/llama_stack/providers/inline/scoring/basic/scoring_fn/ifeval_scoring_fn.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from llama_stack.apis.scoring import ScoringResultRow from llama_stack.apis.scoring_functions import ScoringFnParams @@ -28,9 +28,9 @@ class IfEvalScoringFn(RegisteredBaseScoringFn): async def score_row( self, - input_row: Dict[str, Any], - scoring_fn_identifier: Optional[str] = None, - scoring_params: Optional[ScoringFnParams] = None, + input_row: dict[str, Any], + scoring_fn_identifier: str | None = None, + scoring_params: ScoringFnParams | None = None, ) -> ScoringResultRow: from ..utils.ifeval_utils import INSTRUCTION_DICT, INSTRUCTION_LIST diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_math_response_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_math_response_scoring_fn.py index d6c78a9ac..d765959a8 100644 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_math_response_scoring_fn.py +++ b/llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_math_response_scoring_fn.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from llama_stack.apis.scoring import ScoringResultRow from llama_stack.apis.scoring_functions import ScoringFnParams, ScoringFnParamsType @@ -28,9 +28,9 @@ class RegexParserMathResponseScoringFn(RegisteredBaseScoringFn): async def score_row( self, - input_row: Dict[str, Any], - scoring_fn_identifier: Optional[str] = None, - scoring_params: Optional[ScoringFnParams] = None, + input_row: dict[str, Any], + scoring_fn_identifier: str | None = None, + scoring_params: ScoringFnParams | None = None, ) -> ScoringResultRow: assert scoring_fn_identifier is not None, "Scoring function identifier not found." fn_def = self.supported_fn_defs_registry[scoring_fn_identifier] diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_scoring_fn.py index 0606a9581..cb336e303 100644 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_scoring_fn.py +++ b/llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_scoring_fn.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. import re -from typing import Any, Dict, Optional +from typing import Any from llama_stack.apis.scoring import ScoringResultRow from llama_stack.apis.scoring_functions import ScoringFnParams, ScoringFnParamsType @@ -28,9 +28,9 @@ class RegexParserScoringFn(RegisteredBaseScoringFn): async def score_row( self, - input_row: Dict[str, Any], - scoring_fn_identifier: Optional[str] = None, - scoring_params: Optional[ScoringFnParams] = None, + input_row: dict[str, Any], + scoring_fn_identifier: str | None = None, + scoring_params: ScoringFnParams | None = None, ) -> ScoringResultRow: assert scoring_fn_identifier is not None, "Scoring function identifier not found." fn_def = self.supported_fn_defs_registry[scoring_fn_identifier] diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/subset_of_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/subset_of_scoring_fn.py index 71defc433..d6e10e6c9 100644 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/subset_of_scoring_fn.py +++ b/llama_stack/providers/inline/scoring/basic/scoring_fn/subset_of_scoring_fn.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from llama_stack.apis.scoring import ScoringResultRow from llama_stack.apis.scoring_functions import ScoringFnParams @@ -26,9 +26,9 @@ class SubsetOfScoringFn(RegisteredBaseScoringFn): async def score_row( self, - input_row: Dict[str, Any], - scoring_fn_identifier: Optional[str] = "subset_of", - scoring_params: Optional[ScoringFnParams] = None, + input_row: dict[str, Any], + scoring_fn_identifier: str | None = "subset_of", + scoring_params: ScoringFnParams | None = None, ) -> ScoringResultRow: expected_answer = input_row["expected_answer"] generated_answer = input_row["generated_answer"] diff --git a/llama_stack/providers/inline/scoring/basic/utils/ifeval_utils.py b/llama_stack/providers/inline/scoring/basic/utils/ifeval_utils.py index 28605159f..b74c3826e 100644 --- a/llama_stack/providers/inline/scoring/basic/utils/ifeval_utils.py +++ b/llama_stack/providers/inline/scoring/basic/utils/ifeval_utils.py @@ -11,8 +11,8 @@ import logging import random import re import string +from collections.abc import Iterable, Sequence from types import MappingProxyType -from typing import Dict, Iterable, List, Optional, Sequence, Union import emoji import langdetect @@ -1673,12 +1673,11 @@ def split_chinese_japanese_hindi(lines: str) -> Iterable[str]: The separator for hindi is '।' """ for line in lines.splitlines(): - for sent in re.findall( + yield from re.findall( r"[^!?。\.\!\?\！\？\．\n।]+[!?。\.\!\?\！\？\．\n।]?", line.strip(), flags=re.U, - ): - yield sent + ) def count_words_cjk(text: str) -> int: @@ -1707,7 +1706,7 @@ def count_words_cjk(text: str) -> int: return non_asian_words_cnt + asian_chars_cnt + emoji_cnt -@functools.lru_cache(maxsize=None) +@functools.cache def _get_sentence_tokenizer(): return nltk.data.load("nltk:tokenizers/punkt/english.pickle") @@ -1719,8 +1718,8 @@ def count_sentences(text): return len(tokenized_sentences) -def get_langid(text: str, lid_path: Optional[str] = None) -> str: - line_langs: List[str] = [] +def get_langid(text: str, lid_path: str | None = None) -> str: + line_langs: list[str] = [] lines = [line.strip() for line in text.split("\n") if len(line.strip()) >= 4] for line in lines: @@ -1741,7 +1740,7 @@ def generate_keywords(num_keywords): """Library of instructions""" -_InstructionArgsDtype = Optional[Dict[str, Union[int, str, Sequence[str]]]] +_InstructionArgsDtype = dict[str, int | str | Sequence[str]] | None _LANGUAGES = LANGUAGE_CODES diff --git a/llama_stack/providers/inline/scoring/basic/utils/math_utils.py b/llama_stack/providers/inline/scoring/basic/utils/math_utils.py index e11fc625b..6840aad14 100644 --- a/llama_stack/providers/inline/scoring/basic/utils/math_utils.py +++ b/llama_stack/providers/inline/scoring/basic/utils/math_utils.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import re -from typing import Sequence +from collections.abc import Sequence from llama_stack.providers.utils.scoring.basic_scoring_utils import time_limit @@ -323,7 +323,7 @@ def _fix_a_slash_b(string: str) -> str: try: ia = int(a) ib = int(b) - assert string == "{}/{}".format(ia, ib) + assert string == f"{ia}/{ib}" new_string = "\\frac{" + str(ia) + "}{" + str(ib) + "}" return new_string except (ValueError, AssertionError): diff --git a/llama_stack/providers/inline/scoring/braintrust/__init__.py b/llama_stack/providers/inline/scoring/braintrust/__init__.py index f1b0112d9..8ea6e9b96 100644 --- a/llama_stack/providers/inline/scoring/braintrust/__init__.py +++ b/llama_stack/providers/inline/scoring/braintrust/__init__.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -18,7 +18,7 @@ class BraintrustProviderDataValidator(BaseModel): async def get_provider_impl( config: BraintrustScoringConfig, - deps: Dict[Api, Any], + deps: dict[Api, Any], ): from .braintrust import BraintrustScoringImpl diff --git a/llama_stack/providers/inline/scoring/braintrust/braintrust.py b/llama_stack/providers/inline/scoring/braintrust/braintrust.py index 3fae83340..d6655d657 100644 --- a/llama_stack/providers/inline/scoring/braintrust/braintrust.py +++ b/llama_stack/providers/inline/scoring/braintrust/braintrust.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. import os -from typing import Any, Dict, List, Optional +from typing import Any from autoevals.llm import Factuality from autoevals.ragas import ( @@ -132,7 +132,7 @@ class BraintrustScoringImpl( async def shutdown(self) -> None: ... - async def list_scoring_functions(self) -> List[ScoringFn]: + async def list_scoring_functions(self) -> list[ScoringFn]: scoring_fn_defs_list = list(self.supported_fn_defs_registry.values()) for f in scoring_fn_defs_list: assert f.identifier.startswith("braintrust"), ( @@ -159,7 +159,7 @@ class BraintrustScoringImpl( async def score_batch( self, dataset_id: str, - scoring_functions: Dict[str, Optional[ScoringFnParams]], + scoring_functions: dict[str, ScoringFnParams | None], save_results_dataset: bool = False, ) -> ScoreBatchResponse: await self.set_api_key() @@ -181,9 +181,7 @@ class BraintrustScoringImpl( results=res.results, ) - async def score_row( - self, input_row: Dict[str, Any], scoring_fn_identifier: Optional[str] = None - ) -> ScoringResultRow: + async def score_row(self, input_row: dict[str, Any], scoring_fn_identifier: str | None = None) -> ScoringResultRow: validate_row_schema(input_row, get_valid_schemas(Api.scoring.value)) await self.set_api_key() assert scoring_fn_identifier is not None, "scoring_fn_identifier cannot be None" @@ -203,8 +201,8 @@ class BraintrustScoringImpl( async def score( self, - input_rows: List[Dict[str, Any]], - scoring_functions: Dict[str, Optional[ScoringFnParams]], + input_rows: list[dict[str, Any]], + scoring_functions: dict[str, ScoringFnParams | None], ) -> ScoreResponse: await self.set_api_key() res = {} diff --git a/llama_stack/providers/inline/scoring/braintrust/config.py b/llama_stack/providers/inline/scoring/braintrust/config.py index d4e0d9bcd..4a80f1e4f 100644 --- a/llama_stack/providers/inline/scoring/braintrust/config.py +++ b/llama_stack/providers/inline/scoring/braintrust/config.py @@ -3,19 +3,19 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field class BraintrustScoringConfig(BaseModel): - openai_api_key: Optional[str] = Field( + openai_api_key: str | None = Field( default=None, description="The OpenAI API Key", ) @classmethod - def sample_run_config(cls, **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs) -> dict[str, Any]: return { "openai_api_key": "${env.OPENAI_API_KEY:}", } diff --git a/llama_stack/providers/inline/scoring/llm_as_judge/__init__.py b/llama_stack/providers/inline/scoring/llm_as_judge/__init__.py index 4a83bfe13..88bf10737 100644 --- a/llama_stack/providers/inline/scoring/llm_as_judge/__init__.py +++ b/llama_stack/providers/inline/scoring/llm_as_judge/__init__.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.distribution.datatypes import Api @@ -12,7 +12,7 @@ from .config import LlmAsJudgeScoringConfig async def get_provider_impl( config: LlmAsJudgeScoringConfig, - deps: Dict[Api, Any], + deps: dict[Api, Any], ): from .scoring import LlmAsJudgeScoringImpl diff --git a/llama_stack/providers/inline/scoring/llm_as_judge/config.py b/llama_stack/providers/inline/scoring/llm_as_judge/config.py index ff63fc5e7..b150ef54c 100644 --- a/llama_stack/providers/inline/scoring/llm_as_judge/config.py +++ b/llama_stack/providers/inline/scoring/llm_as_judge/config.py @@ -3,12 +3,12 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel class LlmAsJudgeScoringConfig(BaseModel): @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return {} diff --git a/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py b/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py index 7f004fbb6..b705cb9b3 100644 --- a/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py +++ b/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Optional +from typing import Any from llama_stack.apis.datasetio import DatasetIO from llama_stack.apis.datasets import Datasets @@ -50,7 +50,7 @@ class LlmAsJudgeScoringImpl( async def shutdown(self) -> None: ... - async def list_scoring_functions(self) -> List[ScoringFn]: + async def list_scoring_functions(self) -> list[ScoringFn]: scoring_fn_defs_list = self.llm_as_judge_fn.get_supported_scoring_fn_defs() for f in self.llm_as_judge_fn.get_supported_scoring_fn_defs(): @@ -66,7 +66,7 @@ class LlmAsJudgeScoringImpl( async def score_batch( self, dataset_id: str, - scoring_functions: Dict[str, Optional[ScoringFnParams]] = None, + scoring_functions: dict[str, ScoringFnParams | None] = None, save_results_dataset: bool = False, ) -> ScoreBatchResponse: dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id) @@ -91,8 +91,8 @@ class LlmAsJudgeScoringImpl( async def score( self, - input_rows: List[Dict[str, Any]], - scoring_functions: Dict[str, Optional[ScoringFnParams]] = None, + input_rows: list[dict[str, Any]], + scoring_functions: dict[str, ScoringFnParams | None] = None, ) -> ScoreResponse: res = {} for scoring_fn_id in scoring_functions.keys(): diff --git a/llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/llm_as_judge_scoring_fn.py b/llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/llm_as_judge_scoring_fn.py index f4e8ab0aa..51cdf6c3f 100644 --- a/llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/llm_as_judge_scoring_fn.py +++ b/llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/llm_as_judge_scoring_fn.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. import re -from typing import Any, Dict, Optional +from typing import Any from llama_stack.apis.inference.inference import Inference, UserMessage from llama_stack.apis.scoring import ScoringResultRow @@ -30,9 +30,9 @@ class LlmAsJudgeScoringFn(RegisteredBaseScoringFn): async def score_row( self, - input_row: Dict[str, Any], - scoring_fn_identifier: Optional[str] = None, - scoring_params: Optional[ScoringFnParams] = None, + input_row: dict[str, Any], + scoring_fn_identifier: str | None = None, + scoring_params: ScoringFnParams | None = None, ) -> ScoringResultRow: assert scoring_fn_identifier is not None, "Scoring function identifier not found." fn_def = self.supported_fn_defs_registry[scoring_fn_identifier] diff --git a/llama_stack/providers/inline/telemetry/meta_reference/__init__.py b/llama_stack/providers/inline/telemetry/meta_reference/__init__.py index 23468c5d0..09e97136a 100644 --- a/llama_stack/providers/inline/telemetry/meta_reference/__init__.py +++ b/llama_stack/providers/inline/telemetry/meta_reference/__init__.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.distribution.datatypes import Api @@ -13,7 +13,7 @@ from .config import TelemetryConfig, TelemetrySink __all__ = ["TelemetryConfig", "TelemetrySink"] -async def get_provider_impl(config: TelemetryConfig, deps: Dict[Api, Any]): +async def get_provider_impl(config: TelemetryConfig, deps: dict[Api, Any]): from .telemetry import TelemetryAdapter impl = TelemetryAdapter(config, deps) diff --git a/llama_stack/providers/inline/telemetry/meta_reference/config.py b/llama_stack/providers/inline/telemetry/meta_reference/config.py index 57312f41f..af53bfd9c 100644 --- a/llama_stack/providers/inline/telemetry/meta_reference/config.py +++ b/llama_stack/providers/inline/telemetry/meta_reference/config.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Any, Dict, List +from typing import Any from pydantic import BaseModel, Field, field_validator @@ -30,10 +30,10 @@ class TelemetryConfig(BaseModel): ) service_name: str = Field( # service name is always the same, use zero-width space to avoid clutter - default="​", + default="", description="The service name to use for telemetry", ) - sinks: List[TelemetrySink] = Field( + sinks: list[TelemetrySink] = Field( default=[TelemetrySink.CONSOLE, TelemetrySink.SQLITE], description="List of telemetry sinks to enable (possible values: otel, sqlite, console)", ) @@ -50,9 +50,9 @@ class TelemetryConfig(BaseModel): return v @classmethod - def sample_run_config(cls, __distro_dir__: str, db_name: str = "trace_store.db") -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, db_name: str = "trace_store.db") -> dict[str, Any]: return { - "service_name": "${env.OTEL_SERVICE_NAME:​}", + "service_name": "${env.OTEL_SERVICE_NAME:}", "sinks": "${env.TELEMETRY_SINKS:console,sqlite}", - "sqlite_db_path": "${env.SQLITE_DB_PATH:" + __distro_dir__ + "/" + db_name + "}", + "sqlite_db_path": "${env.SQLITE_STORE_DIR:" + __distro_dir__ + "}/" + db_name, } diff --git a/llama_stack/providers/inline/telemetry/meta_reference/console_span_processor.py b/llama_stack/providers/inline/telemetry/meta_reference/console_span_processor.py index b909d32ef..ff1914c15 100644 --- a/llama_stack/providers/inline/telemetry/meta_reference/console_span_processor.py +++ b/llama_stack/providers/inline/telemetry/meta_reference/console_span_processor.py @@ -78,7 +78,7 @@ class ConsoleSpanProcessor(SpanProcessor): severity = event.attributes.get("severity", "info") message = event.attributes.get("message", event.name) - if isinstance(message, (dict, list)): + if isinstance(message, dict | list): message = json.dumps(message, indent=2) severity_colors = { diff --git a/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py b/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py index 9b23c8229..9295d5cab 100644 --- a/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py +++ b/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import threading -from typing import Any, Dict, List, Optional +from typing import Any from opentelemetry import metrics, trace from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter @@ -60,7 +60,7 @@ def is_tracing_enabled(tracer): class TelemetryAdapter(TelemetryDatasetMixin, Telemetry): - def __init__(self, config: TelemetryConfig, deps: Dict[Api, Any]) -> None: + def __init__(self, config: TelemetryConfig, deps: dict[Api, Any]) -> None: self.config = config self.datasetio_api = deps.get(Api.datasetio) self.meter = None @@ -231,10 +231,10 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry): async def query_traces( self, - attribute_filters: Optional[List[QueryCondition]] = None, - limit: Optional[int] = 100, - offset: Optional[int] = 0, - order_by: Optional[List[str]] = None, + attribute_filters: list[QueryCondition] | None = None, + limit: int | None = 100, + offset: int | None = 0, + order_by: list[str] | None = None, ) -> QueryTracesResponse: return QueryTracesResponse( data=await self.trace_store.query_traces( @@ -254,8 +254,8 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry): async def get_span_tree( self, span_id: str, - attributes_to_return: Optional[List[str]] = None, - max_depth: Optional[int] = None, + attributes_to_return: list[str] | None = None, + max_depth: int | None = None, ) -> QuerySpanTreeResponse: return QuerySpanTreeResponse( data=await self.trace_store.get_span_tree( diff --git a/llama_stack/providers/inline/tool_runtime/code_interpreter/__init__.py b/llama_stack/providers/inline/tool_runtime/code_interpreter/__init__.py deleted file mode 100644 index 8317ce793..000000000 --- a/llama_stack/providers/inline/tool_runtime/code_interpreter/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from typing import Any, Dict - -from .config import CodeInterpreterToolConfig - -__all__ = ["CodeInterpreterToolConfig", "CodeInterpreterToolRuntimeImpl"] - - -async def get_provider_impl(config: CodeInterpreterToolConfig, _deps: Dict[str, Any]): - from .code_interpreter import CodeInterpreterToolRuntimeImpl - - impl = CodeInterpreterToolRuntimeImpl(config) - await impl.initialize() - return impl diff --git a/llama_stack/providers/inline/tool_runtime/code_interpreter/code_env_prefix.py b/llama_stack/providers/inline/tool_runtime/code_interpreter/code_env_prefix.py deleted file mode 100644 index 9c5f642ea..000000000 --- a/llama_stack/providers/inline/tool_runtime/code_interpreter/code_env_prefix.py +++ /dev/null @@ -1,131 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -import errno - -# Disabling potentially dangerous functions -import os as _os -from functools import partial - -os_funcs_to_disable = [ - "kill", - "system", - "putenv", - "remove", - "removedirs", - "rmdir", - "fchdir", - "setuid", - "fork", - "forkpty", - "killpg", - "rename", - "renames", - "truncate", - "replace", - # "unlink", # Commenting as this was blocking matpltlib from rendering plots correctly - "fchmod", - "fchown", - "chmod", - "chown", - "chroot", - "fchdir", - "lchflags", - "lchmod", - "lchown", - "chdir", -] - - -def call_not_allowed(*args, **kwargs): - raise OSError(errno.EPERM, "Call are not permitted in this environment") - - -for func_name in os_funcs_to_disable: - if hasattr(_os, func_name): - setattr(_os, func_name, partial(call_not_allowed, _func_name=f"os.{func_name}")) - -import shutil as _shutil - -for func_name in ["rmtree", "move", "chown"]: - if hasattr(_shutil, func_name): - setattr( - _shutil, - func_name, - partial(call_not_allowed, _func_name=f"shutil.{func_name}"), - ) - -import subprocess as _subprocess - - -def popen_not_allowed(*args, **kwargs): - raise _subprocess.CalledProcessError( - -1, - args[0] if args else "unknown", - stderr="subprocess.Popen is not allowed in this environment", - ) - - -_subprocess.Popen = popen_not_allowed # type: ignore - - -import atexit as _atexit -import builtins as _builtins -import io as _io -import json as _json -import sys as _sys - -# NB! The following "unused" imports crucial, make sure not not to remove -# them with linters - they're used in code_execution.py -from contextlib import ( # noqa - contextmanager as _contextmanager, -) -from multiprocessing.connection import Connection as _Connection - -# Mangle imports to avoid polluting model execution namespace. - -_IO_SINK = _io.StringIO() -_NETWORK_TIMEOUT = 5 -_NETWORK_CONNECTIONS = None - - -def _open_connections(): - global _NETWORK_CONNECTIONS - if _NETWORK_CONNECTIONS is not None: - # Ensure connections only opened once. - return _NETWORK_CONNECTIONS - req_w_fd, resp_r_fd = _sys.argv[1], _sys.argv[2] - req_con = _Connection(int(req_w_fd), readable=False) - resp_con = _Connection(int(resp_r_fd), writable=False) - _NETWORK_CONNECTIONS = (req_con, resp_con) - return _NETWORK_CONNECTIONS - - -_builtins._open_connections = _open_connections # type: ignore - - -@_atexit.register -def _close_connections(): - global _NETWORK_CONNECTIONS - if _NETWORK_CONNECTIONS is None: - return - for con in _NETWORK_CONNECTIONS: - con.close() - del _NETWORK_CONNECTIONS - - -def _network_call(request): - # NOTE: We communicate with the parent process in json, encoded - # in raw bytes. We do this because native send/recv methods use - # pickle which involves execution of arbitrary code. - _open_connections() - req_con, resp_con = _NETWORK_CONNECTIONS - - req_con.send_bytes(_json.dumps(request).encode("utf-8")) - if resp_con.poll(timeout=_NETWORK_TIMEOUT) is None: - raise Exception(f"Network request timed out: {_json.dumps(request)}") - else: - return _json.loads(resp_con.recv_bytes().decode("utf-8")) diff --git a/llama_stack/providers/inline/tool_runtime/code_interpreter/code_execution.py b/llama_stack/providers/inline/tool_runtime/code_interpreter/code_execution.py deleted file mode 100644 index 6106cf741..000000000 --- a/llama_stack/providers/inline/tool_runtime/code_interpreter/code_execution.py +++ /dev/null @@ -1,257 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -import base64 -import json -import multiprocessing -import os -import re -import subprocess -import sys -import tempfile -import textwrap -import time -from dataclasses import dataclass -from datetime import datetime -from io import BytesIO -from pathlib import Path -from typing import List - -from PIL import Image - -from .utils import get_code_env_prefix - -TOOLS_ATTACHMENT_KEY = "__tools_attachment__" -TOOLS_ATTACHMENT_KEY_REGEX = re.compile(r"__tools_attachment__=(\{.*?\})") - -DIRNAME = Path(__file__).parent - -CODE_EXEC_TIMEOUT = 20 -CODE_ENV_PREFIX = get_code_env_prefix() - -STDOUTERR_SINK_WRAPPER_TEMPLATE = """\ -with _redirect_stdout(_IO_SINK), _redirect_stderr(_IO_SINK): -{code}\ -""" - -TRYEXCEPT_WRAPPER_TEMPLATE = """\ -try: -{code} -except: - pass\ -""" - - -def generate_bwrap_command(bind_dirs: List[str]) -> str: - """ - Generate the bwrap command string for binding all - directories in the current directory read-only. - """ - bwrap_args = "" - bwrap_args += "--ro-bind / / " - # Add the --dev flag to mount device files - bwrap_args += "--dev /dev " - for d in bind_dirs: - bwrap_args += f"--bind {d} {d} " - - # Add the --unshare-all flag to isolate the sandbox from the rest of the system - bwrap_args += "--unshare-all " - # Add the --die-with-parent flag to ensure the child process dies when bwrap's parent dies - bwrap_args += "--die-with-parent " - return bwrap_args - - -@dataclass -class CodeExecutionContext: - matplotlib_dump_dir: str - - -@dataclass -class CodeExecutionRequest: - scripts: List[str] - only_last_cell_stdouterr: bool = True - only_last_cell_fail: bool = True - seed: int = 0 - strip_fpaths_in_stderr: bool = True - use_bwrap: bool = True - - -class CodeExecutor: - def __init__(self, context: CodeExecutionContext): - self.context = context - - def execute(self, req: CodeExecutionRequest) -> dict: - scripts = req.scripts - for i in range(len(scripts) - 1): - if req.only_last_cell_stdouterr: - scripts[i] = STDOUTERR_SINK_WRAPPER_TEMPLATE.format(code=textwrap.indent(scripts[i], " " * 4)) - if req.only_last_cell_fail: - scripts[i] = TRYEXCEPT_WRAPPER_TEMPLATE.format(code=textwrap.indent(scripts[i], " " * 4)) - - # Seeds prefix: - seed = req.seed - seeds_prefix = f"""\ -def _set_seeds(): - import random - random.seed({seed}) - import numpy as np - np.random.seed({seed}) -_set_seeds()\ -""" - - script = "\n\n".join([seeds_prefix] + [CODE_ENV_PREFIX] + scripts) - with tempfile.TemporaryDirectory() as dpath: - code_fpath = os.path.join(dpath, "code.py") - with open(code_fpath, "w") as f: - f.write(script) - - try: - python_path = os.environ.get("PYTHONPATH", "") - env = dict( - os.environ, - PYTHONHASHSEED=str(seed), - MPLCONFIGDIR=dpath, - MPLBACKEND="module://matplotlib_custom_backend", - PYTHONPATH=f"{DIRNAME}:{python_path}", - ) - - if req.use_bwrap: - bwrap_prefix = "bwrap " + generate_bwrap_command(bind_dirs=[dpath]) - cmd = [*bwrap_prefix.split(), sys.executable, "-c", script] - else: - cmd = [sys.executable, "-c", script] - - stdout, stderr, returncode = do_subprocess( - cmd=cmd, - env=env, - ctx=self.context, - ) - - stderr = stderr.strip() - if req.strip_fpaths_in_stderr: - pattern = r'File "([^"]+)", line (\d+)' - stderr = re.sub(pattern, r"line \2", stderr) - - return { - "process_status": "completed", - "returncode": returncode, - "stdout": stdout.strip(), - "stderr": stderr, - } - - except subprocess.TimeoutExpired: - return { - "process_status": "timeout", - "stdout": "Timed out", - "stderr": "Timed out", - } - - except Exception as e: - return { - "process_status": "error", - "error_type": type(e).__name__, - "stderr": str(e), - "stdout": str(e), - } - - -def process_matplotlib_response(response, matplotlib_dump_dir: str): - image_data = response["image_data"] - # Convert the base64 string to a bytes object - images_raw = [base64.b64decode(d["image_base64"]) for d in image_data] - # Create a list of PIL images from the bytes objects - images = [Image.open(BytesIO(img)) for img in images_raw] - # Create a list of image paths - image_paths = [] - for i, img in enumerate(images): - # create new directory for each day to better organize data: - dump_dname = datetime.today().strftime("%Y-%m-%d") # noqa: DTZ002 - we don't care about timezones here since we are displaying the date - dump_dpath = Path(matplotlib_dump_dir, dump_dname) - dump_dpath.mkdir(parents=True, exist_ok=True) - # save image into a file - dump_fname = f"matplotlib_{str(time.time()).replace('.', '_')}_{i}.png" - dump_fpath = dump_dpath / dump_fname - img.save(dump_fpath, "PNG") - image_paths.append(str(dump_fpath)) - - # this is kind of convoluted, we send back this response to the subprocess which - # prints it out - info = { - "filepath": str(image_paths[-1]), - "mimetype": "image/png", - } - return f"{TOOLS_ATTACHMENT_KEY}={json.dumps(info)}" - - -def execute_subprocess_request(request, ctx: CodeExecutionContext): - "Route requests from the subprocess (via network Pipes) to the internet/tools." - if request["type"] == "matplotlib": - return process_matplotlib_response(request, ctx.matplotlib_dump_dir) - else: - raise Exception(f"Unrecognised network request type: {request['type']}") - - -def do_subprocess(*, cmd: list, env: dict, ctx: CodeExecutionContext): - # Create Pipes to be used for any external tool/network requests. - req_r, req_w = multiprocessing.Pipe(duplex=False) - resp_r, resp_w = multiprocessing.Pipe(duplex=False) - - cmd += [str(req_w.fileno()), str(resp_r.fileno())] - proc = subprocess.Popen( - cmd, - pass_fds=(req_w.fileno(), resp_r.fileno()), - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - close_fds=True, - env=env, - ) - - # Close unnecessary fds. - req_w.close() - resp_r.close() - - pipe_close = False - done_read = False - start = time.monotonic() - while proc.poll() is None and not pipe_close: - if req_r.poll(0.1): - # NB: Python pipe semantics for poll and recv mean that - # poll() returns True is a pipe is closed. - # CF old school PEP from '09 - # https://bugs.python.org/issue5573 - try: - request = json.loads(req_r.recv_bytes().decode("utf-8")) - response = execute_subprocess_request(request, ctx) - - resp_w.send_bytes(json.dumps(response).encode("utf-8")) - except EOFError: - # The request pipe is closed - set a marker to exit - # after the next attempt at reading stdout/stderr. - pipe_close = True - - try: - # If lots has been printed, pipe might be full but - # proc cannot exit until all the stdout/stderr - # been written/read. - stdout, stderr = proc.communicate(timeout=0.3) - done_read = True - except subprocess.TimeoutExpired: - # The program has not terminated. Ignore it, there - # may be more network/tool requests. - continue - if time.monotonic() - start > CODE_EXEC_TIMEOUT: - proc.terminate() - raise subprocess.TimeoutExpired(cmd, CODE_EXEC_TIMEOUT) - - if not done_read: - # Solve race condition where process terminates before - # we hit the while loop. - stdout, stderr = proc.communicate(timeout=0.3) - - resp_w.close() - req_r.close() - return stdout, stderr, proc.returncode diff --git a/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py b/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py deleted file mode 100644 index 10ac2fcc6..000000000 --- a/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - - -import asyncio -import logging -import os -import tempfile -from typing import Any, Dict, Optional - -from llama_stack.apis.common.content_types import URL -from llama_stack.apis.tools import ( - ListToolDefsResponse, - Tool, - ToolDef, - ToolInvocationResult, - ToolParameter, - ToolRuntime, -) -from llama_stack.providers.datatypes import ToolsProtocolPrivate - -from .code_execution import CodeExecutionContext, CodeExecutionRequest, CodeExecutor -from .config import CodeInterpreterToolConfig - -log = logging.getLogger(__name__) - - -class CodeInterpreterToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime): - def __init__(self, config: CodeInterpreterToolConfig): - self.config = config - ctx = CodeExecutionContext( - matplotlib_dump_dir=tempfile.mkdtemp(), - ) - self.code_executor = CodeExecutor(ctx) - - async def initialize(self): - pass - - async def register_tool(self, tool: Tool) -> None: - pass - - async def unregister_tool(self, tool_id: str) -> None: - return - - async def list_runtime_tools( - self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None - ) -> ListToolDefsResponse: - return ListToolDefsResponse( - data=[ - ToolDef( - name="code_interpreter", - description="Execute code", - parameters=[ - ToolParameter( - name="code", - description="The code to execute", - parameter_type="string", - ), - ], - ) - ] - ) - - async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> ToolInvocationResult: - script = kwargs["code"] - # Use environment variable to control bwrap usage - force_disable_bwrap = os.environ.get("DISABLE_CODE_SANDBOX", "").lower() in ("1", "true", "yes") - req = CodeExecutionRequest(scripts=[script], use_bwrap=not force_disable_bwrap) - res = await asyncio.to_thread(self.code_executor.execute, req) - pieces = [res["process_status"]] - for out_type in ["stdout", "stderr"]: - res_out = res[out_type] - if res_out != "": - pieces.extend([f"[{out_type}]", res_out, f"[/{out_type}]"]) - if out_type == "stderr": - log.error(f"ipython tool error: ↓\n{res_out}") - return ToolInvocationResult(content="\n".join(pieces)) diff --git a/llama_stack/providers/inline/tool_runtime/code_interpreter/config.py b/llama_stack/providers/inline/tool_runtime/code_interpreter/config.py deleted file mode 100644 index 7de1ec453..000000000 --- a/llama_stack/providers/inline/tool_runtime/code_interpreter/config.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from typing import Any, Dict - -from pydantic import BaseModel - - -class CodeInterpreterToolConfig(BaseModel): - @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: - return {} diff --git a/llama_stack/providers/inline/tool_runtime/code_interpreter/matplotlib_custom_backend.py b/llama_stack/providers/inline/tool_runtime/code_interpreter/matplotlib_custom_backend.py deleted file mode 100644 index 6454358a5..000000000 --- a/llama_stack/providers/inline/tool_runtime/code_interpreter/matplotlib_custom_backend.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -""" -A custom Matplotlib backend that overrides the show method to return image bytes. -""" - -import base64 -import io -import json as _json -import logging - -import matplotlib -from matplotlib.backend_bases import FigureManagerBase - -# Import necessary components from Matplotlib -from matplotlib.backends.backend_agg import FigureCanvasAgg - -log = logging.getLogger(__name__) - - -class CustomFigureCanvas(FigureCanvasAgg): - def show(self): - # Save the figure to a BytesIO object - buf = io.BytesIO() - self.print_png(buf) - image_bytes = buf.getvalue() - buf.close() - return image_bytes - - -class CustomFigureManager(FigureManagerBase): - def __init__(self, canvas, num): - super().__init__(canvas, num) - - -# Mimic module initialization that integrates with the Matplotlib backend system -def _create_figure_manager(num, *args, **kwargs): - """ - Create a custom figure manager instance. - """ - FigureClass = kwargs.pop("FigureClass", None) # noqa: N806 - if FigureClass is None: - from matplotlib.figure import Figure - - FigureClass = Figure # noqa: N806 - fig = FigureClass(*args, **kwargs) - canvas = CustomFigureCanvas(fig) - manager = CustomFigureManager(canvas, num) - return manager - - -def show(): - """ - Handle all figures and potentially return their images as bytes. - - This function iterates over all figures registered with the custom backend, - renders them as images in bytes format, and could return a list of bytes objects, - one for each figure, or handle them as needed. - """ - image_data = [] - for manager in matplotlib._pylab_helpers.Gcf.get_all_fig_managers(): - # Get the figure from the manager - fig = manager.canvas.figure - buf = io.BytesIO() # Create a buffer for the figure - fig.savefig(buf, format="png") # Save the figure to the buffer in PNG format - buf.seek(0) # Go to the beginning of the buffer - image_bytes = buf.getvalue() # Retrieve bytes value - image_base64 = base64.b64encode(image_bytes).decode("utf-8") - image_data.append({"image_base64": image_base64}) - buf.close() - - # The _open_connections method is dynamically made available to - # the interpreter by bundling code from "code_env_prefix.py" -- by literally prefixing it -- and - # then "eval"ing it within a sandboxed interpreter. - req_con, resp_con = _open_connections() # noqa: F821 - - _json_dump = _json.dumps( - { - "type": "matplotlib", - "image_data": image_data, - } - ) - req_con.send_bytes(_json_dump.encode("utf-8")) - resp = _json.loads(resp_con.recv_bytes().decode("utf-8")) - log.info(resp) - - -FigureCanvas = CustomFigureCanvas -FigureManager = CustomFigureManager diff --git a/llama_stack/providers/inline/tool_runtime/code_interpreter/utils.py b/llama_stack/providers/inline/tool_runtime/code_interpreter/utils.py deleted file mode 100644 index d6f539a39..000000000 --- a/llama_stack/providers/inline/tool_runtime/code_interpreter/utils.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -import os - -DIR = os.path.dirname(os.path.realpath(__file__)) -CODE_ENV_PREFIX_FILE = os.path.join(DIR, "code_env_prefix.py") -CODE_ENV_PREFIX = None - - -def get_code_env_prefix() -> str: - global CODE_ENV_PREFIX - - if CODE_ENV_PREFIX is None: - with open(CODE_ENV_PREFIX_FILE, "r") as f: - CODE_ENV_PREFIX = f.read() - - return CODE_ENV_PREFIX diff --git a/llama_stack/providers/inline/tool_runtime/rag/__init__.py b/llama_stack/providers/inline/tool_runtime/rag/__init__.py index 0ef3c35e9..f9a6e5c55 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/__init__.py +++ b/llama_stack/providers/inline/tool_runtime/rag/__init__.py @@ -4,14 +4,14 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.providers.datatypes import Api from .config import RagToolRuntimeConfig -async def get_provider_impl(config: RagToolRuntimeConfig, deps: Dict[Api, Any]): +async def get_provider_impl(config: RagToolRuntimeConfig, deps: dict[Api, Any]): from .memory import MemoryToolRuntimeImpl impl = MemoryToolRuntimeImpl(config, deps[Api.vector_io], deps[Api.inference]) diff --git a/llama_stack/providers/inline/tool_runtime/rag/config.py b/llama_stack/providers/inline/tool_runtime/rag/config.py index c75c3fc51..43ba78e65 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/config.py +++ b/llama_stack/providers/inline/tool_runtime/rag/config.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel class RagToolRuntimeConfig(BaseModel): @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return {} diff --git a/llama_stack/providers/inline/tool_runtime/rag/memory.py b/llama_stack/providers/inline/tool_runtime/rag/memory.py index 97c53d454..df0257718 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/memory.py +++ b/llama_stack/providers/inline/tool_runtime/rag/memory.py @@ -8,7 +8,7 @@ import asyncio import logging import secrets import string -from typing import Any, Dict, List, Optional +from typing import Any from pydantic import TypeAdapter @@ -33,6 +33,7 @@ from llama_stack.apis.tools import ( ) from llama_stack.apis.vector_io import QueryChunksResponse, VectorIO from llama_stack.providers.datatypes import ToolsProtocolPrivate +from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str from llama_stack.providers.utils.memory.vector_store import ( content_from_doc, make_overlapped_chunks, @@ -73,7 +74,7 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime): async def insert( self, - documents: List[RAGDocument], + documents: list[RAGDocument], vector_db_id: str, chunk_size_in_tokens: int = 512, ) -> None: @@ -100,8 +101,8 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime): async def query( self, content: InterleavedContent, - vector_db_ids: List[str], - query_config: Optional[RAGQueryConfig] = None, + vector_db_ids: list[str], + query_config: RAGQueryConfig | None = None, ) -> RAGQueryResult: if not vector_db_ids: return RAGQueryResult(content=None) @@ -122,7 +123,7 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime): ) for vector_db_id in vector_db_ids ] - results: List[QueryChunksResponse] = await asyncio.gather(*tasks) + results: list[QueryChunksResponse] = await asyncio.gather(*tasks) chunks = [c for r in results for c in r.chunks] scores = [s for r in results for s in r.scores] @@ -153,6 +154,11 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime): ) ) picked.append(TextContentItem(text="END of knowledge_search tool results.\n")) + picked.append( + TextContentItem( + text=f'The above results were retrieved to help answer the user\'s query: "{interleaved_content_as_str(content)}". Use them as supporting information only in answering this query.\n', + ) + ) return RAGQueryResult( content=picked, @@ -162,7 +168,7 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime): ) async def list_runtime_tools( - self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None + self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None ) -> ListToolDefsResponse: # Parameters are not listed since these methods are not yet invoked automatically # by the LLM. The method is only implemented so things like /tools can list without @@ -187,7 +193,7 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime): ] ) - async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> ToolInvocationResult: + async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult: vector_db_ids = kwargs.get("vector_db_ids", []) query_config = kwargs.get("query_config") if query_config: diff --git a/llama_stack/providers/inline/vector_io/chroma/__init__.py b/llama_stack/providers/inline/vector_io/chroma/__init__.py index f39188b46..2e0efb8a1 100644 --- a/llama_stack/providers/inline/vector_io/chroma/__init__.py +++ b/llama_stack/providers/inline/vector_io/chroma/__init__.py @@ -4,14 +4,14 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.providers.datatypes import Api from .config import ChromaVectorIOConfig -async def get_provider_impl(config: ChromaVectorIOConfig, deps: Dict[Api, Any]): +async def get_provider_impl(config: ChromaVectorIOConfig, deps: dict[Api, Any]): from llama_stack.providers.remote.vector_io.chroma.chroma import ( ChromaVectorIOAdapter, ) diff --git a/llama_stack/providers/inline/vector_io/chroma/config.py b/llama_stack/providers/inline/vector_io/chroma/config.py index 1e333fe92..81e2f289e 100644 --- a/llama_stack/providers/inline/vector_io/chroma/config.py +++ b/llama_stack/providers/inline/vector_io/chroma/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -13,5 +13,5 @@ class ChromaVectorIOConfig(BaseModel): db_path: str @classmethod - def sample_run_config(cls, db_path: str = "${env.CHROMADB_PATH}", **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, db_path: str = "${env.CHROMADB_PATH}", **kwargs: Any) -> dict[str, Any]: return {"db_path": db_path} diff --git a/llama_stack/providers/inline/vector_io/faiss/__init__.py b/llama_stack/providers/inline/vector_io/faiss/__init__.py index fc8ce70b4..68a1dee66 100644 --- a/llama_stack/providers/inline/vector_io/faiss/__init__.py +++ b/llama_stack/providers/inline/vector_io/faiss/__init__.py @@ -4,14 +4,14 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.providers.datatypes import Api from .config import FaissVectorIOConfig -async def get_provider_impl(config: FaissVectorIOConfig, deps: Dict[Api, Any]): +async def get_provider_impl(config: FaissVectorIOConfig, deps: dict[Api, Any]): from .faiss import FaissVectorIOAdapter assert isinstance(config, FaissVectorIOConfig), f"Unexpected config type: {type(config)}" diff --git a/llama_stack/providers/inline/vector_io/faiss/config.py b/llama_stack/providers/inline/vector_io/faiss/config.py index fa6e5bede..cbcbb1762 100644 --- a/llama_stack/providers/inline/vector_io/faiss/config.py +++ b/llama_stack/providers/inline/vector_io/faiss/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -20,7 +20,7 @@ class FaissVectorIOConfig(BaseModel): kvstore: KVStoreConfig @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return { "kvstore": SqliteKVStoreConfig.sample_run_config( __distro_dir__=__distro_dir__, diff --git a/llama_stack/providers/inline/vector_io/faiss/faiss.py b/llama_stack/providers/inline/vector_io/faiss/faiss.py index 20c795650..5d5b1da35 100644 --- a/llama_stack/providers/inline/vector_io/faiss/faiss.py +++ b/llama_stack/providers/inline/vector_io/faiss/faiss.py @@ -9,7 +9,7 @@ import base64 import io import json import logging -from typing import Any, Dict, List, Optional +from typing import Any import faiss import numpy as np @@ -84,7 +84,7 @@ class FaissIndex(EmbeddingIndex): await self.kvstore.delete(f"{FAISS_INDEX_PREFIX}{self.bank_id}") - async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray): + async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): # Add dimension check embedding_dim = embeddings.shape[1] if len(embeddings.shape) > 1 else embeddings.shape[0] if embedding_dim != self.index.d: @@ -159,7 +159,7 @@ class FaissVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): inference_api=self.inference_api, ) - async def list_vector_dbs(self) -> List[VectorDB]: + async def list_vector_dbs(self) -> list[VectorDB]: return [i.vector_db for i in self.cache.values()] async def unregister_vector_db(self, vector_db_id: str) -> None: @@ -176,8 +176,8 @@ class FaissVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): async def insert_chunks( self, vector_db_id: str, - chunks: List[Chunk], - ttl_seconds: Optional[int] = None, + chunks: list[Chunk], + ttl_seconds: int | None = None, ) -> None: index = self.cache.get(vector_db_id) if index is None: @@ -189,7 +189,7 @@ class FaissVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): self, vector_db_id: str, query: InterleavedContent, - params: Optional[Dict[str, Any]] = None, + params: dict[str, Any] | None = None, ) -> QueryChunksResponse: index = self.cache.get(vector_db_id) if index is None: diff --git a/llama_stack/providers/inline/vector_io/milvus/__init__.py b/llama_stack/providers/inline/vector_io/milvus/__init__.py index d88a3b005..fe3a1f7f9 100644 --- a/llama_stack/providers/inline/vector_io/milvus/__init__.py +++ b/llama_stack/providers/inline/vector_io/milvus/__init__.py @@ -4,14 +4,14 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.providers.datatypes import Api from .config import MilvusVectorIOConfig -async def get_provider_impl(config: MilvusVectorIOConfig, deps: Dict[Api, Any]): +async def get_provider_impl(config: MilvusVectorIOConfig, deps: dict[Api, Any]): from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusVectorIOAdapter impl = MilvusVectorIOAdapter(config, deps[Api.inference]) diff --git a/llama_stack/providers/inline/vector_io/milvus/config.py b/llama_stack/providers/inline/vector_io/milvus/config.py index 0e11d8c7c..eb22b5276 100644 --- a/llama_stack/providers/inline/vector_io/milvus/config.py +++ b/llama_stack/providers/inline/vector_io/milvus/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -16,5 +16,5 @@ class MilvusVectorIOConfig(BaseModel): db_path: str @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return {"db_path": "${env.MILVUS_DB_PATH}"} diff --git a/llama_stack/providers/inline/vector_io/qdrant/__init__.py b/llama_stack/providers/inline/vector_io/qdrant/__init__.py index 8f0b91c61..ee33b3797 100644 --- a/llama_stack/providers/inline/vector_io/qdrant/__init__.py +++ b/llama_stack/providers/inline/vector_io/qdrant/__init__.py @@ -4,14 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Dict - from llama_stack.providers.datatypes import Api, ProviderSpec from .config import QdrantVectorIOConfig -async def get_adapter_impl(config: QdrantVectorIOConfig, deps: Dict[Api, ProviderSpec]): +async def get_adapter_impl(config: QdrantVectorIOConfig, deps: dict[Api, ProviderSpec]): from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter impl = QdrantVectorIOAdapter(config, deps[Api.inference]) diff --git a/llama_stack/providers/inline/vector_io/qdrant/config.py b/llama_stack/providers/inline/vector_io/qdrant/config.py index 282e951b0..283724b41 100644 --- a/llama_stack/providers/inline/vector_io/qdrant/config.py +++ b/llama_stack/providers/inline/vector_io/qdrant/config.py @@ -5,7 +5,7 @@ # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -17,7 +17,7 @@ class QdrantVectorIOConfig(BaseModel): path: str @classmethod - def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]: return { "path": "${env.QDRANT_PATH:~/.llama/" + __distro_dir__ + "}/" + "qdrant.db", } diff --git a/llama_stack/providers/inline/vector_io/sqlite_vec/__init__.py b/llama_stack/providers/inline/vector_io/sqlite_vec/__init__.py index 2380eb0ef..6db176eda 100644 --- a/llama_stack/providers/inline/vector_io/sqlite_vec/__init__.py +++ b/llama_stack/providers/inline/vector_io/sqlite_vec/__init__.py @@ -4,14 +4,14 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from llama_stack.providers.datatypes import Api from .config import SQLiteVectorIOConfig -async def get_provider_impl(config: SQLiteVectorIOConfig, deps: Dict[Api, Any]): +async def get_provider_impl(config: SQLiteVectorIOConfig, deps: dict[Api, Any]): from .sqlite_vec import SQLiteVecVectorIOAdapter assert isinstance(config, SQLiteVectorIOConfig), f"Unexpected config type: {type(config)}" diff --git a/llama_stack/providers/inline/vector_io/sqlite_vec/config.py b/llama_stack/providers/inline/vector_io/sqlite_vec/config.py index 906c19689..cb806cb39 100644 --- a/llama_stack/providers/inline/vector_io/sqlite_vec/config.py +++ b/llama_stack/providers/inline/vector_io/sqlite_vec/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -13,7 +13,7 @@ class SQLiteVectorIOConfig(BaseModel): db_path: str @classmethod - def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]: return { "db_path": "${env.SQLITE_STORE_DIR:" + __distro_dir__ + "}/" + "sqlite_vec.db", } diff --git a/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py b/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py index 5f7671138..ab4384021 100644 --- a/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py +++ b/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py @@ -10,7 +10,7 @@ import logging import sqlite3 import struct import uuid -from typing import Any, Dict, List, Optional +from typing import Any import numpy as np import sqlite_vec @@ -25,7 +25,7 @@ from llama_stack.providers.utils.memory.vector_store import EmbeddingIndex, Vect logger = logging.getLogger(__name__) -def serialize_vector(vector: List[float]) -> bytes: +def serialize_vector(vector: list[float]) -> bytes: """Serialize a list of floats into a compact binary representation.""" return struct.pack(f"{len(vector)}f", *vector) @@ -98,7 +98,7 @@ class SQLiteVecIndex(EmbeddingIndex): await asyncio.to_thread(_drop_tables) - async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray, batch_size: int = 500): + async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray, batch_size: int = 500): """ Add new chunks along with their embeddings using batch inserts. For each chunk, we insert its JSON into the metadata table and then insert its @@ -209,7 +209,7 @@ class SQLiteVecVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): def __init__(self, config, inference_api: Inference) -> None: self.config = config self.inference_api = inference_api - self.cache: Dict[str, VectorDBWithIndex] = {} + self.cache: dict[str, VectorDBWithIndex] = {} async def initialize(self) -> None: def _setup_connection(): @@ -264,7 +264,7 @@ class SQLiteVecVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): index = await SQLiteVecIndex.create(vector_db.embedding_dimension, self.config.db_path, vector_db.identifier) self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api) - async def list_vector_dbs(self) -> List[VectorDB]: + async def list_vector_dbs(self) -> list[VectorDB]: return [v.vector_db for v in self.cache.values()] async def unregister_vector_db(self, vector_db_id: str) -> None: @@ -286,7 +286,7 @@ class SQLiteVecVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): await asyncio.to_thread(_delete_vector_db_from_registry) - async def insert_chunks(self, vector_db_id: str, chunks: List[Chunk], ttl_seconds: Optional[int] = None) -> None: + async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None: if vector_db_id not in self.cache: raise ValueError(f"Vector DB {vector_db_id} not found. Found: {list(self.cache.keys())}") # The VectorDBWithIndex helper is expected to compute embeddings via the inference_api @@ -294,7 +294,7 @@ class SQLiteVecVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): await self.cache[vector_db_id].insert_chunks(chunks) async def query_chunks( - self, vector_db_id: str, query: Any, params: Optional[Dict[str, Any]] = None + self, vector_db_id: str, query: Any, params: dict[str, Any] | None = None ) -> QueryChunksResponse: if vector_db_id not in self.cache: raise ValueError(f"Vector DB {vector_db_id} not found") @@ -303,5 +303,5 @@ class SQLiteVecVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): def generate_chunk_id(document_id: str, chunk_text: str) -> str: """Generate a unique chunk ID using a hash of document ID and chunk text.""" - hash_input = f"{document_id}:{chunk_text}".encode("utf-8") + hash_input = f"{document_id}:{chunk_text}".encode() return str(uuid.UUID(hashlib.md5(hash_input).hexdigest())) diff --git a/llama_stack/providers/registry/agents.py b/llama_stack/providers/registry/agents.py index 3ed59304d..e0801a8d1 100644 --- a/llama_stack/providers/registry/agents.py +++ b/llama_stack/providers/registry/agents.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List from llama_stack.providers.datatypes import ( Api, @@ -14,7 +13,7 @@ from llama_stack.providers.datatypes import ( from llama_stack.providers.utils.kvstore import kvstore_dependencies -def available_providers() -> List[ProviderSpec]: +def available_providers() -> list[ProviderSpec]: return [ InlineProviderSpec( api=Api.agents, diff --git a/llama_stack/providers/registry/datasetio.py b/llama_stack/providers/registry/datasetio.py index f83dcbc60..152cc9cb9 100644 --- a/llama_stack/providers/registry/datasetio.py +++ b/llama_stack/providers/registry/datasetio.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List from llama_stack.providers.datatypes import ( AdapterSpec, @@ -15,7 +14,7 @@ from llama_stack.providers.datatypes import ( ) -def available_providers() -> List[ProviderSpec]: +def available_providers() -> list[ProviderSpec]: return [ InlineProviderSpec( api=Api.datasetio, @@ -36,4 +35,15 @@ def available_providers() -> List[ProviderSpec]: config_class="llama_stack.providers.remote.datasetio.huggingface.HuggingfaceDatasetIOConfig", ), ), + remote_provider_spec( + api=Api.datasetio, + adapter=AdapterSpec( + adapter_type="nvidia", + pip_packages=[ + "datasets", + ], + module="llama_stack.providers.remote.datasetio.nvidia", + config_class="llama_stack.providers.remote.datasetio.nvidia.NvidiaDatasetIOConfig", + ), + ), ] diff --git a/llama_stack/providers/registry/eval.py b/llama_stack/providers/registry/eval.py index f3e42c531..c9c29bbe0 100644 --- a/llama_stack/providers/registry/eval.py +++ b/llama_stack/providers/registry/eval.py @@ -4,12 +4,11 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List -from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec +from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec -def available_providers() -> List[ProviderSpec]: +def available_providers() -> list[ProviderSpec]: return [ InlineProviderSpec( api=Api.eval, @@ -25,4 +24,22 @@ def available_providers() -> List[ProviderSpec]: Api.agents, ], ), + remote_provider_spec( + api=Api.eval, + adapter=AdapterSpec( + adapter_type="nvidia", + pip_packages=[ + "requests", + ], + module="llama_stack.providers.remote.eval.nvidia", + config_class="llama_stack.providers.remote.eval.nvidia.NVIDIAEvalConfig", + ), + api_dependencies=[ + Api.datasetio, + Api.datasets, + Api.scoring, + Api.inference, + Api.agents, + ], + ), ] diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index ba02dc7fa..7b49ef09b 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List from llama_stack.providers.datatypes import ( AdapterSpec, @@ -29,7 +28,7 @@ META_REFERENCE_DEPS = [ ] -def available_providers() -> List[ProviderSpec]: +def available_providers() -> list[ProviderSpec]: return [ InlineProviderSpec( api=Api.inference, @@ -227,6 +226,16 @@ def available_providers() -> List[ProviderSpec]: provider_data_validator="llama_stack.providers.remote.inference.fireworks_openai_compat.config.FireworksProviderDataValidator", ), ), + remote_provider_spec( + api=Api.inference, + adapter=AdapterSpec( + adapter_type="llama-openai-compat", + pip_packages=["litellm"], + module="llama_stack.providers.remote.inference.llama_openai_compat", + config_class="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaCompatConfig", + provider_data_validator="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaProviderDataValidator", + ), + ), remote_provider_spec( api=Api.inference, adapter=AdapterSpec( @@ -287,4 +296,14 @@ def available_providers() -> List[ProviderSpec]: provider_data_validator="llama_stack.providers.remote.inference.passthrough.PassthroughProviderDataValidator", ), ), + remote_provider_spec( + api=Api.inference, + adapter=AdapterSpec( + adapter_type="watsonx", + pip_packages=["ibm_watson_machine_learning"], + module="llama_stack.providers.remote.inference.watsonx", + config_class="llama_stack.providers.remote.inference.watsonx.WatsonXConfig", + provider_data_validator="llama_stack.providers.remote.inference.watsonx.WatsonXProviderDataValidator", + ), + ), ] diff --git a/llama_stack/providers/registry/post_training.py b/llama_stack/providers/registry/post_training.py index 4d10fcf3b..35567c07d 100644 --- a/llama_stack/providers/registry/post_training.py +++ b/llama_stack/providers/registry/post_training.py @@ -4,12 +4,11 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec -def available_providers() -> List[ProviderSpec]: +def available_providers() -> list[ProviderSpec]: return [ InlineProviderSpec( api=Api.post_training, diff --git a/llama_stack/providers/registry/safety.py b/llama_stack/providers/registry/safety.py index 54dc51034..c209da092 100644 --- a/llama_stack/providers/registry/safety.py +++ b/llama_stack/providers/registry/safety.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List from llama_stack.providers.datatypes import ( AdapterSpec, @@ -15,7 +14,7 @@ from llama_stack.providers.datatypes import ( ) -def available_providers() -> List[ProviderSpec]: +def available_providers() -> list[ProviderSpec]: return [ InlineProviderSpec( api=Api.safety, diff --git a/llama_stack/providers/registry/scoring.py b/llama_stack/providers/registry/scoring.py index ca09be984..7980d6a13 100644 --- a/llama_stack/providers/registry/scoring.py +++ b/llama_stack/providers/registry/scoring.py @@ -4,12 +4,11 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec -def available_providers() -> List[ProviderSpec]: +def available_providers() -> list[ProviderSpec]: return [ InlineProviderSpec( api=Api.scoring, diff --git a/llama_stack/providers/registry/telemetry.py b/llama_stack/providers/registry/telemetry.py index fc249f3e2..14da06126 100644 --- a/llama_stack/providers/registry/telemetry.py +++ b/llama_stack/providers/registry/telemetry.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List from llama_stack.providers.datatypes import ( Api, @@ -13,7 +12,7 @@ from llama_stack.providers.datatypes import ( ) -def available_providers() -> List[ProviderSpec]: +def available_providers() -> list[ProviderSpec]: return [ InlineProviderSpec( api=Api.telemetry, diff --git a/llama_stack/providers/registry/tool_runtime.py b/llama_stack/providers/registry/tool_runtime.py index 95ea2dcf9..b9194810e 100644 --- a/llama_stack/providers/registry/tool_runtime.py +++ b/llama_stack/providers/registry/tool_runtime.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List from llama_stack.providers.datatypes import ( AdapterSpec, @@ -15,7 +14,7 @@ from llama_stack.providers.datatypes import ( ) -def available_providers() -> List[ProviderSpec]: +def available_providers() -> list[ProviderSpec]: return [ InlineProviderSpec( api=Api.tool_runtime, @@ -36,13 +35,6 @@ def available_providers() -> List[ProviderSpec]: config_class="llama_stack.providers.inline.tool_runtime.rag.config.RagToolRuntimeConfig", api_dependencies=[Api.vector_io, Api.inference], ), - InlineProviderSpec( - api=Api.tool_runtime, - provider_type="inline::code-interpreter", - pip_packages=[], - module="llama_stack.providers.inline.tool_runtime.code_interpreter", - config_class="llama_stack.providers.inline.tool_runtime.code_interpreter.config.CodeInterpreterToolConfig", - ), remote_provider_spec( api=Api.tool_runtime, adapter=AdapterSpec( diff --git a/llama_stack/providers/registry/vector_io.py b/llama_stack/providers/registry/vector_io.py index 93031763d..d888c8420 100644 --- a/llama_stack/providers/registry/vector_io.py +++ b/llama_stack/providers/registry/vector_io.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List from llama_stack.providers.datatypes import ( AdapterSpec, @@ -15,7 +14,7 @@ from llama_stack.providers.datatypes import ( ) -def available_providers() -> List[ProviderSpec]: +def available_providers() -> list[ProviderSpec]: return [ InlineProviderSpec( api=Api.vector_io, diff --git a/llama_stack/providers/remote/datasetio/huggingface/config.py b/llama_stack/providers/remote/datasetio/huggingface/config.py index c06996b6f..38f933728 100644 --- a/llama_stack/providers/remote/datasetio/huggingface/config.py +++ b/llama_stack/providers/remote/datasetio/huggingface/config.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -17,7 +17,7 @@ class HuggingfaceDatasetIOConfig(BaseModel): kvstore: KVStoreConfig @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return { "kvstore": SqliteKVStoreConfig.sample_run_config( __distro_dir__=__distro_dir__, diff --git a/llama_stack/providers/remote/datasetio/huggingface/huggingface.py b/llama_stack/providers/remote/datasetio/huggingface/huggingface.py index 7a17e5e42..baecf45e9 100644 --- a/llama_stack/providers/remote/datasetio/huggingface/huggingface.py +++ b/llama_stack/providers/remote/datasetio/huggingface/huggingface.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Optional +from typing import Any from urllib.parse import parse_qs, urlparse import datasets as hf_datasets @@ -70,8 +70,8 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate): async def iterrows( self, dataset_id: str, - start_index: Optional[int] = None, - limit: Optional[int] = None, + start_index: int | None = None, + limit: int | None = None, ) -> PaginatedResponse: dataset_def = self.dataset_infos[dataset_id] path, params = parse_hf_params(dataset_def) @@ -80,7 +80,7 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate): records = [loaded_dataset[i] for i in range(len(loaded_dataset))] return paginate_records(records, start_index, limit) - async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None: + async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None: dataset_def = self.dataset_infos[dataset_id] path, params = parse_hf_params(dataset_def) loaded_dataset = hf_datasets.load_dataset(path, **params) diff --git a/llama_stack/providers/remote/datasetio/nvidia/README.md b/llama_stack/providers/remote/datasetio/nvidia/README.md new file mode 100644 index 000000000..1d3d15132 --- /dev/null +++ b/llama_stack/providers/remote/datasetio/nvidia/README.md @@ -0,0 +1,74 @@ +# NVIDIA DatasetIO Provider for LlamaStack + +This provider enables dataset management using NVIDIA's NeMo Customizer service. + +## Features + +- Register datasets for fine-tuning LLMs +- Unregister datasets + +## Getting Started + +### Prerequisites + +- LlamaStack with NVIDIA configuration +- Access to Hosted NVIDIA NeMo Microservice +- API key for authentication with the NVIDIA service + +### Setup + +Build the NVIDIA environment: + +```bash +llama stack build --template nvidia --image-type conda +``` + +### Basic Usage using the LlamaStack Python Client + +#### Initialize the client + +```python +import os + +os.environ["NVIDIA_API_KEY"] = "your-api-key" +os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test" +os.environ["NVIDIA_USER_ID"] = "llama-stack-user" +os.environ["NVIDIA_DATASET_NAMESPACE"] = "default" +os.environ["NVIDIA_PROJECT_ID"] = "test-project" +from llama_stack.distribution.library_client import LlamaStackAsLibraryClient + +client = LlamaStackAsLibraryClient("nvidia") +client.initialize() +``` + +#### Register a dataset + +```python +client.datasets.register( + purpose="post-training/messages", + dataset_id="my-training-dataset", + source={"type": "uri", "uri": "hf://datasets/default/sample-dataset"}, + metadata={ + "format": "json", + "description": "Dataset for LLM fine-tuning", + "provider": "nvidia", + }, +) +``` + +#### Get a list of all registered datasets + +```python +datasets = client.datasets.list() +for dataset in datasets: + print(f"Dataset ID: {dataset.identifier}") + print(f"Description: {dataset.metadata.get('description', '')}") + print(f"Source: {dataset.source.uri}") + print("---") +``` + +#### Unregister a dataset + +```python +client.datasets.unregister(dataset_id="my-training-dataset") +``` diff --git a/llama_stack/providers/remote/datasetio/nvidia/__init__.py b/llama_stack/providers/remote/datasetio/nvidia/__init__.py new file mode 100644 index 000000000..418daec8d --- /dev/null +++ b/llama_stack/providers/remote/datasetio/nvidia/__init__.py @@ -0,0 +1,23 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from .config import NvidiaDatasetIOConfig + + +async def get_adapter_impl( + config: NvidiaDatasetIOConfig, + _deps, +): + from .datasetio import NvidiaDatasetIOAdapter + + if not isinstance(config, NvidiaDatasetIOConfig): + raise RuntimeError(f"Unexpected config type: {type(config)}") + + impl = NvidiaDatasetIOAdapter(config) + return impl + + +__all__ = ["get_adapter_impl", "NvidiaDatasetIOAdapter"] diff --git a/llama_stack/providers/remote/datasetio/nvidia/config.py b/llama_stack/providers/remote/datasetio/nvidia/config.py new file mode 100644 index 000000000..e616ce25c --- /dev/null +++ b/llama_stack/providers/remote/datasetio/nvidia/config.py @@ -0,0 +1,61 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import os +import warnings +from typing import Any + +from pydantic import BaseModel, Field + + +class NvidiaDatasetIOConfig(BaseModel): + """Configuration for NVIDIA DatasetIO implementation.""" + + api_key: str | None = Field( + default_factory=lambda: os.getenv("NVIDIA_API_KEY"), + description="The NVIDIA API key.", + ) + + dataset_namespace: str | None = Field( + default_factory=lambda: os.getenv("NVIDIA_DATASET_NAMESPACE", "default"), + description="The NVIDIA dataset namespace.", + ) + + project_id: str | None = Field( + default_factory=lambda: os.getenv("NVIDIA_PROJECT_ID", "test-project"), + description="The NVIDIA project ID.", + ) + + datasets_url: str = Field( + default_factory=lambda: os.getenv("NVIDIA_DATASETS_URL", "http://nemo.test"), + description="Base URL for the NeMo Dataset API", + ) + + # warning for default values + def __post_init__(self): + default_values = [] + if os.getenv("NVIDIA_PROJECT_ID") is None: + default_values.append("project_id='test-project'") + if os.getenv("NVIDIA_DATASET_NAMESPACE") is None: + default_values.append("dataset_namespace='default'") + if os.getenv("NVIDIA_DATASETS_URL") is None: + default_values.append("datasets_url='http://nemo.test'") + + if default_values: + warnings.warn( + f"Using default values: {', '.join(default_values)}. \ + Please set the environment variables to avoid this default behavior.", + stacklevel=2, + ) + + @classmethod + def sample_run_config(cls, **kwargs) -> dict[str, Any]: + return { + "api_key": "${env.NVIDIA_API_KEY:}", + "dataset_namespace": "${env.NVIDIA_DATASET_NAMESPACE:default}", + "project_id": "${env.NVIDIA_PROJECT_ID:test-project}", + "datasets_url": "${env.NVIDIA_DATASETS_URL:http://nemo.test}", + } diff --git a/llama_stack/providers/remote/datasetio/nvidia/datasetio.py b/llama_stack/providers/remote/datasetio/nvidia/datasetio.py new file mode 100644 index 000000000..6a9e2bb58 --- /dev/null +++ b/llama_stack/providers/remote/datasetio/nvidia/datasetio.py @@ -0,0 +1,112 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +import aiohttp + +from llama_stack.apis.common.content_types import URL +from llama_stack.apis.common.responses import PaginatedResponse +from llama_stack.apis.common.type_system import ParamType +from llama_stack.apis.datasets import Dataset + +from .config import NvidiaDatasetIOConfig + + +class NvidiaDatasetIOAdapter: + """Nvidia NeMo DatasetIO API.""" + + def __init__(self, config: NvidiaDatasetIOConfig): + self.config = config + self.headers = {} + + async def _make_request( + self, + method: str, + path: str, + headers: dict[str, Any] | None = None, + params: dict[str, Any] | None = None, + json: dict[str, Any] | None = None, + **kwargs, + ) -> dict[str, Any]: + """Helper method to make HTTP requests to the Customizer API.""" + url = f"{self.config.datasets_url}{path}" + request_headers = self.headers.copy() + + if headers: + request_headers.update(headers) + + async with aiohttp.ClientSession(headers=request_headers) as session: + async with session.request(method, url, params=params, json=json, **kwargs) as response: + if response.status != 200: + error_data = await response.json() + raise Exception(f"API request failed: {error_data}") + return await response.json() + + async def register_dataset( + self, + dataset_def: Dataset, + ) -> Dataset: + """Register a new dataset. + + Args: + dataset_def [Dataset]: The dataset definition. + dataset_id [str]: The ID of the dataset. + source [DataSource]: The source of the dataset. + metadata [Dict[str, Any]]: The metadata of the dataset. + format [str]: The format of the dataset. + description [str]: The description of the dataset. + Returns: + Dataset + """ + ## add warnings for unsupported params + request_body = { + "name": dataset_def.identifier, + "namespace": self.config.dataset_namespace, + "files_url": dataset_def.source.uri, + "project": self.config.project_id, + } + if dataset_def.metadata: + request_body["format"] = dataset_def.metadata.get("format") + request_body["description"] = dataset_def.metadata.get("description") + await self._make_request( + "POST", + "/v1/datasets", + json=request_body, + ) + return dataset_def + + async def update_dataset( + self, + dataset_id: str, + dataset_schema: dict[str, ParamType], + url: URL, + provider_dataset_id: str | None = None, + provider_id: str | None = None, + metadata: dict[str, Any] | None = None, + ) -> None: + raise NotImplementedError("Not implemented") + + async def unregister_dataset( + self, + dataset_id: str, + ) -> None: + await self._make_request( + "DELETE", + f"/v1/datasets/{self.config.dataset_namespace}/{dataset_id}", + headers={"Accept": "application/json", "Content-Type": "application/json"}, + ) + + async def iterrows( + self, + dataset_id: str, + start_index: int | None = None, + limit: int | None = None, + ) -> PaginatedResponse: + raise NotImplementedError("Not implemented") + + async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None: + raise NotImplementedError("Not implemented") diff --git a/llama_stack/providers/remote/eval/__init__.py b/llama_stack/providers/remote/eval/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/llama_stack/providers/remote/eval/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/llama_stack/providers/remote/eval/nvidia/README.md b/llama_stack/providers/remote/eval/nvidia/README.md new file mode 100644 index 000000000..cebc77920 --- /dev/null +++ b/llama_stack/providers/remote/eval/nvidia/README.md @@ -0,0 +1,134 @@ +# NVIDIA NeMo Evaluator Eval Provider + + +## Overview + +For the first integration, Benchmarks are mapped to Evaluation Configs on in the NeMo Evaluator. The full evaluation config object is provided as part of the meta-data. The `dataset_id` and `scoring_functions` are not used. + +Below are a few examples of how to register a benchmark, which in turn will create an evaluation config in NeMo Evaluator and how to trigger an evaluation. + +### Example for register an academic benchmark + +``` +POST /eval/benchmarks +``` +```json +{ + "benchmark_id": "mmlu", + "dataset_id": "", + "scoring_functions": [], + "metadata": { + "type": "mmlu" + } +} +``` + +### Example for register a custom evaluation + +``` +POST /eval/benchmarks +``` +```json +{ + "benchmark_id": "my-custom-benchmark", + "dataset_id": "", + "scoring_functions": [], + "metadata": { + "type": "custom", + "params": { + "parallelism": 8 + }, + "tasks": { + "qa": { + "type": "completion", + "params": { + "template": { + "prompt": "{{prompt}}", + "max_tokens": 200 + } + }, + "dataset": { + "files_url": "hf://datasets/default/sample-basic-test/testing/testing.jsonl" + }, + "metrics": { + "bleu": { + "type": "bleu", + "params": { + "references": [ + "{{ideal_response}}" + ] + } + } + } + } + } + } +} +``` + +### Example for triggering a benchmark/custom evaluation + +``` +POST /eval/benchmarks/{benchmark_id}/jobs +``` +```json +{ + "benchmark_id": "my-custom-benchmark", + "benchmark_config": { + "eval_candidate": { + "type": "model", + "model": "meta-llama/Llama3.1-8B-Instruct", + "sampling_params": { + "max_tokens": 100, + "temperature": 0.7 + } + }, + "scoring_params": {} + } +} +``` + +Response example: +```json +{ + "job_id": "eval-1234", + "status": "in_progress" +} +``` + +### Example for getting the status of a job +``` +GET /eval/benchmarks/{benchmark_id}/jobs/{job_id} +``` + +Response example: +```json +{ + "job_id": "eval-1234", + "status": "in_progress" +} +``` + +### Example for cancelling a job +``` +POST /eval/benchmarks/{benchmark_id}/jobs/{job_id}/cancel +``` + +### Example for getting the results +``` +GET /eval/benchmarks/{benchmark_id}/results +``` +```json +{ + "generations": [], + "scores": { + "{benchmark_id}": { + "score_rows": [], + "aggregated_results": { + "tasks": {}, + "groups": {} + } + } + } +} +``` diff --git a/llama_stack/providers/remote/eval/nvidia/__init__.py b/llama_stack/providers/remote/eval/nvidia/__init__.py new file mode 100644 index 000000000..55e3754f3 --- /dev/null +++ b/llama_stack/providers/remote/eval/nvidia/__init__.py @@ -0,0 +1,31 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. +from typing import Any + +from llama_stack.distribution.datatypes import Api + +from .config import NVIDIAEvalConfig + + +async def get_adapter_impl( + config: NVIDIAEvalConfig, + deps: dict[Api, Any], +): + from .eval import NVIDIAEvalImpl + + impl = NVIDIAEvalImpl( + config, + deps[Api.datasetio], + deps[Api.datasets], + deps[Api.scoring], + deps[Api.inference], + deps[Api.agents], + ) + await impl.initialize() + return impl + + +__all__ = ["get_adapter_impl", "NVIDIAEvalImpl"] diff --git a/llama_stack/providers/remote/eval/nvidia/config.py b/llama_stack/providers/remote/eval/nvidia/config.py new file mode 100644 index 000000000..5c8f9ff76 --- /dev/null +++ b/llama_stack/providers/remote/eval/nvidia/config.py @@ -0,0 +1,29 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. +import os +from typing import Any + +from pydantic import BaseModel, Field + + +class NVIDIAEvalConfig(BaseModel): + """ + Configuration for the NVIDIA NeMo Evaluator microservice endpoint. + + Attributes: + evaluator_url (str): A base url for accessing the NVIDIA evaluation endpoint, e.g. http://localhost:8000. + """ + + evaluator_url: str = Field( + default_factory=lambda: os.getenv("NVIDIA_EVALUATOR_URL", "http://0.0.0.0:7331"), + description="The url for accessing the evaluator service", + ) + + @classmethod + def sample_run_config(cls, **kwargs) -> dict[str, Any]: + return { + "evaluator_url": "${env.NVIDIA_EVALUATOR_URL:http://localhost:7331}", + } diff --git a/llama_stack/providers/remote/eval/nvidia/eval.py b/llama_stack/providers/remote/eval/nvidia/eval.py new file mode 100644 index 000000000..3572de0ef --- /dev/null +++ b/llama_stack/providers/remote/eval/nvidia/eval.py @@ -0,0 +1,154 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. +from typing import Any + +import requests + +from llama_stack.apis.agents import Agents +from llama_stack.apis.benchmarks import Benchmark +from llama_stack.apis.datasetio import DatasetIO +from llama_stack.apis.datasets import Datasets +from llama_stack.apis.inference import Inference +from llama_stack.apis.scoring import Scoring, ScoringResult +from llama_stack.providers.datatypes import BenchmarksProtocolPrivate +from llama_stack.providers.remote.inference.nvidia.models import MODEL_ENTRIES +from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper + +from .....apis.common.job_types import Job, JobStatus +from .....apis.eval.eval import BenchmarkConfig, Eval, EvaluateResponse +from .config import NVIDIAEvalConfig + +DEFAULT_NAMESPACE = "nvidia" + + +class NVIDIAEvalImpl( + Eval, + BenchmarksProtocolPrivate, + ModelRegistryHelper, +): + def __init__( + self, + config: NVIDIAEvalConfig, + datasetio_api: DatasetIO, + datasets_api: Datasets, + scoring_api: Scoring, + inference_api: Inference, + agents_api: Agents, + ) -> None: + self.config = config + self.datasetio_api = datasetio_api + self.datasets_api = datasets_api + self.scoring_api = scoring_api + self.inference_api = inference_api + self.agents_api = agents_api + + ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES) + + async def initialize(self) -> None: ... + + async def shutdown(self) -> None: ... + + async def _evaluator_get(self, path): + """Helper for making GET requests to the evaluator service.""" + response = requests.get(url=f"{self.config.evaluator_url}{path}") + response.raise_for_status() + return response.json() + + async def _evaluator_post(self, path, data): + """Helper for making POST requests to the evaluator service.""" + response = requests.post(url=f"{self.config.evaluator_url}{path}", json=data) + response.raise_for_status() + return response.json() + + async def register_benchmark(self, task_def: Benchmark) -> None: + """Register a benchmark as an evaluation configuration.""" + await self._evaluator_post( + "/v1/evaluation/configs", + { + "namespace": DEFAULT_NAMESPACE, + "name": task_def.benchmark_id, + # metadata is copied to request body as-is + **task_def.metadata, + }, + ) + + async def run_eval( + self, + benchmark_id: str, + benchmark_config: BenchmarkConfig, + ) -> Job: + """Run an evaluation job for a benchmark.""" + model = ( + benchmark_config.eval_candidate.model + if benchmark_config.eval_candidate.type == "model" + else benchmark_config.eval_candidate.config.model + ) + nvidia_model = self.get_provider_model_id(model) or model + + result = await self._evaluator_post( + "/v1/evaluation/jobs", + { + "config": f"{DEFAULT_NAMESPACE}/{benchmark_id}", + "target": {"type": "model", "model": nvidia_model}, + }, + ) + + return Job(job_id=result["id"], status=JobStatus.in_progress) + + async def evaluate_rows( + self, + benchmark_id: str, + input_rows: list[dict[str, Any]], + scoring_functions: list[str], + benchmark_config: BenchmarkConfig, + ) -> EvaluateResponse: + raise NotImplementedError() + + async def job_status(self, benchmark_id: str, job_id: str) -> Job: + """Get the status of an evaluation job. + + EvaluatorStatus: "created", "pending", "running", "cancelled", "cancelling", "failed", "completed". + JobStatus: "scheduled", "in_progress", "completed", "cancelled", "failed" + """ + result = await self._evaluator_get(f"/v1/evaluation/jobs/{job_id}") + result_status = result["status"] + + job_status = JobStatus.failed + if result_status in ["created", "pending"]: + job_status = JobStatus.scheduled + elif result_status in ["running"]: + job_status = JobStatus.in_progress + elif result_status in ["completed"]: + job_status = JobStatus.completed + elif result_status in ["cancelled"]: + job_status = JobStatus.cancelled + + return Job(job_id=job_id, status=job_status) + + async def job_cancel(self, benchmark_id: str, job_id: str) -> None: + """Cancel the evaluation job.""" + await self._evaluator_post(f"/v1/evaluation/jobs/{job_id}/cancel", {}) + + async def job_result(self, benchmark_id: str, job_id: str) -> EvaluateResponse: + """Returns the results of the evaluation job.""" + + job = await self.job_status(benchmark_id, job_id) + status = job.status + if not status or status != JobStatus.completed: + raise ValueError(f"Job {job_id} not completed. Status: {status.value}") + + result = await self._evaluator_get(f"/v1/evaluation/jobs/{job_id}/results") + + return EvaluateResponse( + # TODO: these are stored in detailed results on NeMo Evaluator side; can be added + generations=[], + scores={ + benchmark_id: ScoringResult( + score_rows=[], + aggregated_results=result, + ) + }, + ) diff --git a/llama_stack/providers/remote/inference/anthropic/__init__.py b/llama_stack/providers/remote/inference/anthropic/__init__.py index 3075f856e..8b420a5a0 100644 --- a/llama_stack/providers/remote/inference/anthropic/__init__.py +++ b/llama_stack/providers/remote/inference/anthropic/__init__.py @@ -4,15 +4,13 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Optional - from pydantic import BaseModel from .config import AnthropicConfig class AnthropicProviderDataValidator(BaseModel): - anthropic_api_key: Optional[str] = None + anthropic_api_key: str | None = None async def get_adapter_impl(config: AnthropicConfig, _deps): diff --git a/llama_stack/providers/remote/inference/anthropic/config.py b/llama_stack/providers/remote/inference/anthropic/config.py index 0e9469602..10da0025e 100644 --- a/llama_stack/providers/remote/inference/anthropic/config.py +++ b/llama_stack/providers/remote/inference/anthropic/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -12,7 +12,7 @@ from llama_stack.schema_utils import json_schema_type class AnthropicProviderDataValidator(BaseModel): - anthropic_api_key: Optional[str] = Field( + anthropic_api_key: str | None = Field( default=None, description="API key for Anthropic models", ) @@ -20,13 +20,13 @@ class AnthropicProviderDataValidator(BaseModel): @json_schema_type class AnthropicConfig(BaseModel): - api_key: Optional[str] = Field( + api_key: str | None = Field( default=None, description="API key for Anthropic models", ) @classmethod - def sample_run_config(cls, api_key: str = "${env.ANTHROPIC_API_KEY}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, api_key: str = "${env.ANTHROPIC_API_KEY}", **kwargs) -> dict[str, Any]: return { "api_key": api_key, } diff --git a/llama_stack/providers/remote/inference/bedrock/__init__.py b/llama_stack/providers/remote/inference/bedrock/__init__.py index e72c6ada9..4d98f4999 100644 --- a/llama_stack/providers/remote/inference/bedrock/__init__.py +++ b/llama_stack/providers/remote/inference/bedrock/__init__.py @@ -1,18 +1,18 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. -from .config import BedrockConfig - - -async def get_adapter_impl(config: BedrockConfig, _deps): - from .bedrock import BedrockInferenceAdapter - - assert isinstance(config, BedrockConfig), f"Unexpected config type: {type(config)}" - - impl = BedrockInferenceAdapter(config) - - await impl.initialize() - - return impl +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. +from .config import BedrockConfig + + +async def get_adapter_impl(config: BedrockConfig, _deps): + from .bedrock import BedrockInferenceAdapter + + assert isinstance(config, BedrockConfig), f"Unexpected config type: {type(config)}" + + impl = BedrockInferenceAdapter(config) + + await impl.initialize() + + return impl diff --git a/llama_stack/providers/remote/inference/bedrock/bedrock.py b/llama_stack/providers/remote/inference/bedrock/bedrock.py index f8dbcf31a..0404a578f 100644 --- a/llama_stack/providers/remote/inference/bedrock/bedrock.py +++ b/llama_stack/providers/remote/inference/bedrock/bedrock.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import json -from typing import AsyncGenerator, AsyncIterator, Dict, List, Optional, Union +from collections.abc import AsyncGenerator, AsyncIterator from botocore.client import BaseClient @@ -79,26 +79,26 @@ class BedrockInferenceAdapter( self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> AsyncGenerator: raise NotImplementedError() async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, - ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]: + messages: list[Message], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, + ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]: if sampling_params is None: sampling_params = SamplingParams() model = await self.model_store.get_model(model_id) @@ -151,7 +151,7 @@ class BedrockInferenceAdapter( async for chunk in process_chat_completion_stream_response(stream, request): yield chunk - async def _get_params_for_chat_completion(self, request: ChatCompletionRequest) -> Dict: + async def _get_params_for_chat_completion(self, request: ChatCompletionRequest) -> dict: bedrock_model = request.model sampling_params = request.sampling_params @@ -176,10 +176,10 @@ class BedrockInferenceAdapter( async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: model = await self.model_store.get_model(model_id) embeddings = [] diff --git a/llama_stack/providers/remote/inference/bedrock/config.py b/llama_stack/providers/remote/inference/bedrock/config.py index f2e8930be..5961a2f15 100644 --- a/llama_stack/providers/remote/inference/bedrock/config.py +++ b/llama_stack/providers/remote/inference/bedrock/config.py @@ -1,11 +1,11 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.providers.utils.bedrock.config import BedrockBaseConfig - - -class BedrockConfig(BedrockBaseConfig): - pass +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from llama_stack.providers.utils.bedrock.config import BedrockBaseConfig + + +class BedrockConfig(BedrockBaseConfig): + pass diff --git a/llama_stack/providers/remote/inference/cerebras/cerebras.py b/llama_stack/providers/remote/inference/cerebras/cerebras.py index 3156601be..685375346 100644 --- a/llama_stack/providers/remote/inference/cerebras/cerebras.py +++ b/llama_stack/providers/remote/inference/cerebras/cerebras.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import AsyncGenerator, List, Optional, Union +from collections.abc import AsyncGenerator from cerebras.cloud.sdk import AsyncCerebras @@ -79,10 +79,10 @@ class CerebrasInferenceAdapter( self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -120,15 +120,15 @@ class CerebrasInferenceAdapter( async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages: list[Message], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -166,7 +166,7 @@ class CerebrasInferenceAdapter( async for chunk in process_chat_completion_stream_response(stream, request): yield chunk - async def _get_params(self, request: Union[ChatCompletionRequest, CompletionRequest]) -> dict: + async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict: if request.sampling_params and isinstance(request.sampling_params.strategy, TopKSamplingStrategy): raise ValueError("`top_k` not supported by Cerebras") @@ -188,9 +188,9 @@ class CerebrasInferenceAdapter( async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: raise NotImplementedError() diff --git a/llama_stack/providers/remote/inference/cerebras/config.py b/llama_stack/providers/remote/inference/cerebras/config.py index 81682c980..81312ec76 100644 --- a/llama_stack/providers/remote/inference/cerebras/config.py +++ b/llama_stack/providers/remote/inference/cerebras/config.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import os -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field, SecretStr @@ -20,13 +20,13 @@ class CerebrasImplConfig(BaseModel): default=os.environ.get("CEREBRAS_BASE_URL", DEFAULT_BASE_URL), description="Base URL for the Cerebras API", ) - api_key: Optional[SecretStr] = Field( + api_key: SecretStr | None = Field( default=os.environ.get("CEREBRAS_API_KEY"), description="Cerebras API Key", ) @classmethod - def sample_run_config(cls, **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs) -> dict[str, Any]: return { "base_url": DEFAULT_BASE_URL, "api_key": "${env.CEREBRAS_API_KEY}", diff --git a/llama_stack/providers/remote/inference/cerebras_openai_compat/config.py b/llama_stack/providers/remote/inference/cerebras_openai_compat/config.py index 149c0a202..cb8daff6a 100644 --- a/llama_stack/providers/remote/inference/cerebras_openai_compat/config.py +++ b/llama_stack/providers/remote/inference/cerebras_openai_compat/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -12,7 +12,7 @@ from llama_stack.schema_utils import json_schema_type class CerebrasProviderDataValidator(BaseModel): - cerebras_api_key: Optional[str] = Field( + cerebras_api_key: str | None = Field( default=None, description="API key for Cerebras models", ) @@ -20,7 +20,7 @@ class CerebrasProviderDataValidator(BaseModel): @json_schema_type class CerebrasCompatConfig(BaseModel): - api_key: Optional[str] = Field( + api_key: str | None = Field( default=None, description="The Cerebras API key", ) @@ -31,7 +31,7 @@ class CerebrasCompatConfig(BaseModel): ) @classmethod - def sample_run_config(cls, api_key: str = "${env.CEREBRAS_API_KEY}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, api_key: str = "${env.CEREBRAS_API_KEY}", **kwargs) -> dict[str, Any]: return { "openai_compat_api_base": "https://api.cerebras.ai/v1", "api_key": api_key, diff --git a/llama_stack/providers/remote/inference/databricks/config.py b/llama_stack/providers/remote/inference/databricks/config.py index 1d51125cb..5710dcef3 100644 --- a/llama_stack/providers/remote/inference/databricks/config.py +++ b/llama_stack/providers/remote/inference/databricks/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel, Field @@ -28,7 +28,7 @@ class DatabricksImplConfig(BaseModel): url: str = "${env.DATABRICKS_URL}", api_token: str = "${env.DATABRICKS_API_TOKEN}", **kwargs: Any, - ) -> Dict[str, Any]: + ) -> dict[str, Any]: return { "url": url, "api_token": api_token, diff --git a/llama_stack/providers/remote/inference/databricks/databricks.py b/llama_stack/providers/remote/inference/databricks/databricks.py index 27d96eb7d..5c36eac3e 100644 --- a/llama_stack/providers/remote/inference/databricks/databricks.py +++ b/llama_stack/providers/remote/inference/databricks/databricks.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import AsyncGenerator, List, Optional +from collections.abc import AsyncGenerator from openai import OpenAI @@ -78,25 +78,25 @@ class DatabricksInferenceAdapter( self, model: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> AsyncGenerator: raise NotImplementedError() async def chat_completion( self, model: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages: list[Message], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -146,9 +146,9 @@ class DatabricksInferenceAdapter( async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: raise NotImplementedError() diff --git a/llama_stack/providers/remote/inference/fireworks/config.py b/llama_stack/providers/remote/inference/fireworks/config.py index c21ce4a40..072d558f4 100644 --- a/llama_stack/providers/remote/inference/fireworks/config.py +++ b/llama_stack/providers/remote/inference/fireworks/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field, SecretStr @@ -17,13 +17,13 @@ class FireworksImplConfig(BaseModel): default="https://api.fireworks.ai/inference/v1", description="The URL for the Fireworks server", ) - api_key: Optional[SecretStr] = Field( + api_key: SecretStr | None = Field( default=None, description="The Fireworks.ai API Key", ) @classmethod - def sample_run_config(cls, api_key: str = "${env.FIREWORKS_API_KEY}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, api_key: str = "${env.FIREWORKS_API_KEY}", **kwargs) -> dict[str, Any]: return { "url": "https://api.fireworks.ai/inference/v1", "api_key": api_key, diff --git a/llama_stack/providers/remote/inference/fireworks/fireworks.py b/llama_stack/providers/remote/inference/fireworks/fireworks.py index 48c163c87..b6d3984c6 100644 --- a/llama_stack/providers/remote/inference/fireworks/fireworks.py +++ b/llama_stack/providers/remote/inference/fireworks/fireworks.py @@ -4,7 +4,8 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union +from collections.abc import AsyncGenerator, AsyncIterator +from typing import Any from fireworks.client import Fireworks from openai import AsyncOpenAI @@ -105,10 +106,10 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -146,9 +147,9 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv def _build_options( self, - sampling_params: Optional[SamplingParams], + sampling_params: SamplingParams | None, fmt: ResponseFormat, - logprobs: Optional[LogProbConfig], + logprobs: LogProbConfig | None, ) -> dict: options = get_sampling_options(sampling_params) options.setdefault("max_tokens", 512) @@ -177,15 +178,15 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages: list[Message], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -229,7 +230,7 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv async for chunk in process_chat_completion_stream_response(stream, request): yield chunk - async def _get_params(self, request: Union[ChatCompletionRequest, CompletionRequest]) -> dict: + async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict: input_dict = {} media_present = request_has_media(request) @@ -263,10 +264,10 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: model = await self.model_store.get_model(model_id) @@ -288,24 +289,24 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv async def openai_completion( self, model: str, - prompt: Union[str, List[str], List[int], List[List[int]]], - best_of: Optional[int] = None, - echo: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - presence_penalty: Optional[float] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - guided_choice: Optional[List[str]] = None, - prompt_logprobs: Optional[int] = None, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, ) -> OpenAICompletion: model_obj = await self.model_store.get_model(model) @@ -338,30 +339,63 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv async def openai_chat_completion( self, model: str, - messages: List[OpenAIMessageParam], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: + messages: list[OpenAIMessageParam], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: model_obj = await self.model_store.get_model(model) + + # Divert Llama Models through Llama Stack inference APIs because + # Fireworks chat completions OpenAI-compatible API does not support + # tool calls properly. + llama_model = self.get_llama_model(model_obj.provider_resource_id) + if llama_model: + return await OpenAIChatCompletionToLlamaStackMixin.openai_chat_completion( + self, + model=model, + messages=messages, + frequency_penalty=frequency_penalty, + function_call=function_call, + functions=functions, + logit_bias=logit_bias, + logprobs=logprobs, + max_completion_tokens=max_completion_tokens, + max_tokens=max_tokens, + n=n, + parallel_tool_calls=parallel_tool_calls, + presence_penalty=presence_penalty, + response_format=response_format, + seed=seed, + stop=stop, + stream=stream, + stream_options=stream_options, + temperature=temperature, + tool_choice=tool_choice, + tools=tools, + top_logprobs=top_logprobs, + top_p=top_p, + user=user, + ) + params = await prepare_openai_completion_params( messages=messages, frequency_penalty=frequency_penalty, @@ -387,11 +421,4 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv user=user, ) - # Divert Llama Models through Llama Stack inference APIs because - # Fireworks chat completions OpenAI-compatible API does not support - # tool calls properly. - llama_model = self.get_llama_model(model_obj.provider_resource_id) - if llama_model: - return await OpenAIChatCompletionToLlamaStackMixin.openai_chat_completion(self, model=model, **params) - return await self._get_openai_client().chat.completions.create(model=model_obj.provider_resource_id, **params) diff --git a/llama_stack/providers/remote/inference/fireworks_openai_compat/config.py b/llama_stack/providers/remote/inference/fireworks_openai_compat/config.py index 0263d348a..bf38cdd2b 100644 --- a/llama_stack/providers/remote/inference/fireworks_openai_compat/config.py +++ b/llama_stack/providers/remote/inference/fireworks_openai_compat/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -12,7 +12,7 @@ from llama_stack.schema_utils import json_schema_type class FireworksProviderDataValidator(BaseModel): - fireworks_api_key: Optional[str] = Field( + fireworks_api_key: str | None = Field( default=None, description="API key for Fireworks models", ) @@ -20,7 +20,7 @@ class FireworksProviderDataValidator(BaseModel): @json_schema_type class FireworksCompatConfig(BaseModel): - api_key: Optional[str] = Field( + api_key: str | None = Field( default=None, description="The Fireworks API key", ) @@ -31,7 +31,7 @@ class FireworksCompatConfig(BaseModel): ) @classmethod - def sample_run_config(cls, api_key: str = "${env.FIREWORKS_API_KEY}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, api_key: str = "${env.FIREWORKS_API_KEY}", **kwargs) -> dict[str, Any]: return { "openai_compat_api_base": "https://api.fireworks.ai/inference/v1", "api_key": api_key, diff --git a/llama_stack/providers/remote/inference/gemini/__init__.py b/llama_stack/providers/remote/inference/gemini/__init__.py index dd972f21c..9d35da893 100644 --- a/llama_stack/providers/remote/inference/gemini/__init__.py +++ b/llama_stack/providers/remote/inference/gemini/__init__.py @@ -4,15 +4,13 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Optional - from pydantic import BaseModel from .config import GeminiConfig class GeminiProviderDataValidator(BaseModel): - gemini_api_key: Optional[str] = None + gemini_api_key: str | None = None async def get_adapter_impl(config: GeminiConfig, _deps): diff --git a/llama_stack/providers/remote/inference/gemini/config.py b/llama_stack/providers/remote/inference/gemini/config.py index 30c8d9913..63ef4de01 100644 --- a/llama_stack/providers/remote/inference/gemini/config.py +++ b/llama_stack/providers/remote/inference/gemini/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -12,7 +12,7 @@ from llama_stack.schema_utils import json_schema_type class GeminiProviderDataValidator(BaseModel): - gemini_api_key: Optional[str] = Field( + gemini_api_key: str | None = Field( default=None, description="API key for Gemini models", ) @@ -20,13 +20,13 @@ class GeminiProviderDataValidator(BaseModel): @json_schema_type class GeminiConfig(BaseModel): - api_key: Optional[str] = Field( + api_key: str | None = Field( default=None, description="API key for Gemini models", ) @classmethod - def sample_run_config(cls, api_key: str = "${env.GEMINI_API_KEY}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, api_key: str = "${env.GEMINI_API_KEY}", **kwargs) -> dict[str, Any]: return { "api_key": api_key, } diff --git a/llama_stack/providers/remote/inference/groq/config.py b/llama_stack/providers/remote/inference/groq/config.py index 8a1204b0b..fe060507a 100644 --- a/llama_stack/providers/remote/inference/groq/config.py +++ b/llama_stack/providers/remote/inference/groq/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -12,7 +12,7 @@ from llama_stack.schema_utils import json_schema_type class GroqProviderDataValidator(BaseModel): - groq_api_key: Optional[str] = Field( + groq_api_key: str | None = Field( default=None, description="API key for Groq models", ) @@ -20,7 +20,7 @@ class GroqProviderDataValidator(BaseModel): @json_schema_type class GroqConfig(BaseModel): - api_key: Optional[str] = Field( + api_key: str | None = Field( # The Groq client library loads the GROQ_API_KEY environment variable by default default=None, description="The Groq API key", @@ -32,7 +32,7 @@ class GroqConfig(BaseModel): ) @classmethod - def sample_run_config(cls, api_key: str = "${env.GROQ_API_KEY}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, api_key: str = "${env.GROQ_API_KEY}", **kwargs) -> dict[str, Any]: return { "url": "https://api.groq.com", "api_key": api_key, diff --git a/llama_stack/providers/remote/inference/groq/groq.py b/llama_stack/providers/remote/inference/groq/groq.py index f3f14e9af..27d7d7961 100644 --- a/llama_stack/providers/remote/inference/groq/groq.py +++ b/llama_stack/providers/remote/inference/groq/groq.py @@ -4,7 +4,8 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, AsyncIterator, Dict, List, Optional, Union +from collections.abc import AsyncIterator +from typing import Any from openai import AsyncOpenAI @@ -59,29 +60,29 @@ class GroqInferenceAdapter(LiteLLMOpenAIMixin): async def openai_chat_completion( self, model: str, - messages: List[OpenAIMessageParam], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: + messages: list[OpenAIMessageParam], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: model_obj = await self.model_store.get_model(model) # Groq does not support json_schema response format, so we need to convert it to json_object diff --git a/llama_stack/providers/remote/inference/groq_openai_compat/config.py b/llama_stack/providers/remote/inference/groq_openai_compat/config.py index 4b90b4576..481f740f9 100644 --- a/llama_stack/providers/remote/inference/groq_openai_compat/config.py +++ b/llama_stack/providers/remote/inference/groq_openai_compat/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -12,7 +12,7 @@ from llama_stack.schema_utils import json_schema_type class GroqProviderDataValidator(BaseModel): - groq_api_key: Optional[str] = Field( + groq_api_key: str | None = Field( default=None, description="API key for Groq models", ) @@ -20,7 +20,7 @@ class GroqProviderDataValidator(BaseModel): @json_schema_type class GroqCompatConfig(BaseModel): - api_key: Optional[str] = Field( + api_key: str | None = Field( default=None, description="The Groq API key", ) @@ -31,7 +31,7 @@ class GroqCompatConfig(BaseModel): ) @classmethod - def sample_run_config(cls, api_key: str = "${env.GROQ_API_KEY}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, api_key: str = "${env.GROQ_API_KEY}", **kwargs) -> dict[str, Any]: return { "openai_compat_api_base": "https://api.groq.com/openai/v1", "api_key": api_key, diff --git a/llama_stack/providers/remote/inference/llama_openai_compat/__init__.py b/llama_stack/providers/remote/inference/llama_openai_compat/__init__.py new file mode 100644 index 000000000..a6fb37cad --- /dev/null +++ b/llama_stack/providers/remote/inference/llama_openai_compat/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from llama_stack.apis.inference import Inference + +from .config import LlamaCompatConfig + + +async def get_adapter_impl(config: LlamaCompatConfig, _deps) -> Inference: + # import dynamically so the import is used only when it is needed + from .llama import LlamaCompatInferenceAdapter + + adapter = LlamaCompatInferenceAdapter(config) + return adapter diff --git a/llama_stack/providers/remote/inference/llama_openai_compat/config.py b/llama_stack/providers/remote/inference/llama_openai_compat/config.py new file mode 100644 index 000000000..57bc7240d --- /dev/null +++ b/llama_stack/providers/remote/inference/llama_openai_compat/config.py @@ -0,0 +1,38 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from pydantic import BaseModel, Field + +from llama_stack.schema_utils import json_schema_type + + +class LlamaProviderDataValidator(BaseModel): + llama_api_key: str | None = Field( + default=None, + description="API key for api.llama models", + ) + + +@json_schema_type +class LlamaCompatConfig(BaseModel): + api_key: str | None = Field( + default=None, + description="The Llama API key", + ) + + openai_compat_api_base: str = Field( + default="https://api.llama.com/compat/v1/", + description="The URL for the Llama API server", + ) + + @classmethod + def sample_run_config(cls, api_key: str = "${env.LLAMA_API_KEY}", **kwargs) -> dict[str, Any]: + return { + "openai_compat_api_base": "https://api.llama.com/compat/v1/", + "api_key": api_key, + } diff --git a/llama_stack/providers/remote/inference/llama_openai_compat/llama.py b/llama_stack/providers/remote/inference/llama_openai_compat/llama.py new file mode 100644 index 000000000..29b5e889a --- /dev/null +++ b/llama_stack/providers/remote/inference/llama_openai_compat/llama.py @@ -0,0 +1,34 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from llama_stack.providers.remote.inference.llama_openai_compat.config import ( + LlamaCompatConfig, +) +from llama_stack.providers.utils.inference.litellm_openai_mixin import ( + LiteLLMOpenAIMixin, +) + +from .models import MODEL_ENTRIES + + +class LlamaCompatInferenceAdapter(LiteLLMOpenAIMixin): + _config: LlamaCompatConfig + + def __init__(self, config: LlamaCompatConfig): + LiteLLMOpenAIMixin.__init__( + self, + model_entries=MODEL_ENTRIES, + api_key_from_config=config.api_key, + provider_data_api_key_field="llama_api_key", + openai_compat_api_base=config.openai_compat_api_base, + ) + self.config = config + + async def initialize(self): + await super().initialize() + + async def shutdown(self): + await super().shutdown() diff --git a/llama_stack/providers/remote/inference/llama_openai_compat/models.py b/llama_stack/providers/remote/inference/llama_openai_compat/models.py new file mode 100644 index 000000000..6285e98e1 --- /dev/null +++ b/llama_stack/providers/remote/inference/llama_openai_compat/models.py @@ -0,0 +1,25 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from llama_stack.models.llama.sku_types import CoreModelId +from llama_stack.providers.utils.inference.model_registry import ( + build_hf_repo_model_entry, +) + +MODEL_ENTRIES = [ + build_hf_repo_model_entry( + "Llama-3.3-70B-Instruct", + CoreModelId.llama3_3_70b_instruct.value, + ), + build_hf_repo_model_entry( + "Llama-4-Scout-17B-16E-Instruct-FP8", + CoreModelId.llama4_scout_17b_16e_instruct.value, + ), + build_hf_repo_model_entry( + "Llama-4-Maverick-17B-128E-Instruct-FP8", + CoreModelId.llama4_maverick_17b_128e_instruct.value, + ), +] diff --git a/llama_stack/providers/remote/inference/nvidia/NVIDIA.md b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md new file mode 100644 index 000000000..a353c67f5 --- /dev/null +++ b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md @@ -0,0 +1,85 @@ +# NVIDIA Inference Provider for LlamaStack + +This provider enables running inference using NVIDIA NIM. + +## Features +- Endpoints for completions, chat completions, and embeddings for registered models + +## Getting Started + +### Prerequisites + +- LlamaStack with NVIDIA configuration +- Access to NVIDIA NIM deployment +- NIM for model to use for inference is deployed + +### Setup + +Build the NVIDIA environment: + +```bash +llama stack build --template nvidia --image-type conda +``` + +### Basic Usage using the LlamaStack Python Client + +#### Initialize the client + +```python +import os + +os.environ["NVIDIA_API_KEY"] = ( + "" # Required if using hosted NIM endpoint. If self-hosted, not required. +) +os.environ["NVIDIA_BASE_URL"] = "http://nim.test" # NIM URL + +from llama_stack.distribution.library_client import LlamaStackAsLibraryClient + +client = LlamaStackAsLibraryClient("nvidia") +client.initialize() +``` + +### Create Completion + +```python +response = client.completion( + model_id="meta-llama/Llama-3.1-8b-Instruct", + content="Complete the sentence using one word: Roses are red, violets are :", + stream=False, + sampling_params={ + "max_tokens": 50, + }, +) +print(f"Response: {response.content}") +``` + +### Create Chat Completion + +```python +response = client.chat_completion( + model_id="meta-llama/Llama-3.1-8b-Instruct", + messages=[ + { + "role": "system", + "content": "You must respond to each message with only one word", + }, + { + "role": "user", + "content": "Complete the sentence using one word: Roses are red, violets are:", + }, + ], + stream=False, + sampling_params={ + "max_tokens": 50, + }, +) +print(f"Response: {response.completion_message.content}") +``` + +### Create Embeddings +```python +response = client.embeddings( + model_id="meta-llama/Llama-3.1-8b-Instruct", contents=["foo", "bar", "baz"] +) +print(f"Embeddings: {response.embeddings}") +``` diff --git a/llama_stack/providers/remote/inference/nvidia/config.py b/llama_stack/providers/remote/inference/nvidia/config.py index abd34b498..4c449edc2 100644 --- a/llama_stack/providers/remote/inference/nvidia/config.py +++ b/llama_stack/providers/remote/inference/nvidia/config.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import os -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field, SecretStr @@ -39,7 +39,7 @@ class NVIDIAConfig(BaseModel): default_factory=lambda: os.getenv("NVIDIA_BASE_URL", "https://integrate.api.nvidia.com"), description="A base url for accessing the NVIDIA NIM", ) - api_key: Optional[SecretStr] = Field( + api_key: SecretStr | None = Field( default_factory=lambda: os.getenv("NVIDIA_API_KEY"), description="The NVIDIA API key, only needed of using the hosted service", ) @@ -47,10 +47,15 @@ class NVIDIAConfig(BaseModel): default=60, description="Timeout for the HTTP requests", ) + append_api_version: bool = Field( + default_factory=lambda: os.getenv("NVIDIA_APPEND_API_VERSION", "True").lower() != "false", + description="When set to false, the API version will not be appended to the base_url. By default, it is true.", + ) @classmethod - def sample_run_config(cls, **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs) -> dict[str, Any]: return { "url": "${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com}", "api_key": "${env.NVIDIA_API_KEY:}", + "append_api_version": "${env.NVIDIA_APPEND_API_VERSION:True}", } diff --git a/llama_stack/providers/remote/inference/nvidia/models.py b/llama_stack/providers/remote/inference/nvidia/models.py index 964125148..127a6ca59 100644 --- a/llama_stack/providers/remote/inference/nvidia/models.py +++ b/llama_stack/providers/remote/inference/nvidia/models.py @@ -48,6 +48,10 @@ MODEL_ENTRIES = [ "meta/llama-3.2-90b-vision-instruct", CoreModelId.llama3_2_90b_vision_instruct.value, ), + build_hf_repo_model_entry( + "meta/llama-3.3-70b-instruct", + CoreModelId.llama3_3_70b_instruct.value, + ), # NeMo Retriever Text Embedding models - # # https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html diff --git a/llama_stack/providers/remote/inference/nvidia/nvidia.py b/llama_stack/providers/remote/inference/nvidia/nvidia.py index 15f0e72a1..333486fe4 100644 --- a/llama_stack/providers/remote/inference/nvidia/nvidia.py +++ b/llama_stack/providers/remote/inference/nvidia/nvidia.py @@ -6,8 +6,9 @@ import logging import warnings +from collections.abc import AsyncIterator from functools import lru_cache -from typing import Any, AsyncIterator, Dict, List, Optional, Union +from typing import Any from openai import APIConnectionError, AsyncOpenAI, BadRequestError @@ -33,7 +34,6 @@ from llama_stack.apis.inference import ( TextTruncation, ToolChoice, ToolConfig, - ToolDefinition, ) from llama_stack.apis.inference.inference import ( OpenAIChatCompletion, @@ -42,7 +42,11 @@ from llama_stack.apis.inference.inference import ( OpenAIMessageParam, OpenAIResponseFormatParam, ) -from llama_stack.models.llama.datatypes import ToolPromptFormat +from llama_stack.apis.models import Model, ModelType +from llama_stack.models.llama.datatypes import ToolDefinition, ToolPromptFormat +from llama_stack.providers.utils.inference import ( + ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR, +) from llama_stack.providers.utils.inference.model_registry import ( ModelRegistryHelper, ) @@ -120,21 +124,29 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): "meta/llama-3.2-90b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct", } - base_url = f"{self._config.url}/v1" + base_url = f"{self._config.url}/v1" if self._config.append_api_version else self._config.url + if _is_nvidia_hosted(self._config) and provider_model_id in special_model_urls: base_url = special_model_urls[provider_model_id] - return _get_client_for_base_url(base_url) + async def _get_provider_model_id(self, model_id: str) -> str: + if not self.model_store: + raise RuntimeError("Model store is not set") + model = await self.model_store.get_model(model_id) + if model is None: + raise ValueError(f"Model {model_id} is unknown") + return model.provider_model_id + async def completion( self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - ) -> Union[CompletionResponse, AsyncIterator[CompletionResponseStreamChunk]]: + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]: if sampling_params is None: sampling_params = SamplingParams() if content_has_media(content): @@ -144,7 +156,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): # removing this health check as NeMo customizer endpoint health check is returning 404 # await check_health(self._config) # this raises errors - provider_model_id = self.get_provider_model_id(model_id) + provider_model_id = await self._get_provider_model_id(model_id) request = convert_completion_request( request=CompletionRequest( model=provider_model_id, @@ -171,24 +183,24 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: if any(content_has_media(content) for content in contents): raise NotImplementedError("Media is not supported") # - # Llama Stack: contents = List[str] | List[InterleavedContentItem] + # Llama Stack: contents = list[str] | list[InterleavedContentItem] # -> - # OpenAI: input = str | List[str] + # OpenAI: input = str | list[str] # - # we can ignore str and always pass List[str] to OpenAI + # we can ignore str and always pass list[str] to OpenAI # flat_contents = [content.text if isinstance(content, TextContentItem) else content for content in contents] input = [content.text if isinstance(content, TextContentItem) else content for content in flat_contents] - model = self.get_provider_model_id(model_id) + provider_model_id = await self._get_provider_model_id(model_id) extra_body = {} @@ -211,8 +223,8 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): extra_body["input_type"] = task_type_options[task_type] try: - response = await self._get_client(model).embeddings.create( - model=model, + response = await self._get_client(provider_model_id).embeddings.create( + model=provider_model_id, input=input, extra_body=extra_body, ) @@ -220,25 +232,25 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): raise ValueError(f"Failed to get embeddings: {e}") from e # - # OpenAI: CreateEmbeddingResponse(data=[Embedding(embedding=List[float], ...)], ...) + # OpenAI: CreateEmbeddingResponse(data=[Embedding(embedding=list[float], ...)], ...) # -> - # Llama Stack: EmbeddingsResponse(embeddings=List[List[float]]) + # Llama Stack: EmbeddingsResponse(embeddings=list[list[float]]) # return EmbeddingsResponse(embeddings=[embedding.embedding for embedding in response.data]) async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, - ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]: + messages: list[Message], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, + ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]: if sampling_params is None: sampling_params = SamplingParams() if tool_prompt_format: @@ -246,10 +258,10 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): # await check_health(self._config) # this raises errors - provider_model_id = self.get_provider_model_id(model_id) + provider_model_id = await self._get_provider_model_id(model_id) request = await convert_chat_completion_request( request=ChatCompletionRequest( - model=self.get_provider_model_id(model_id), + model=provider_model_id, messages=messages, sampling_params=sampling_params, response_format=response_format, @@ -275,26 +287,26 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): async def openai_completion( self, model: str, - prompt: Union[str, List[str], List[int], List[List[int]]], - best_of: Optional[int] = None, - echo: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - presence_penalty: Optional[float] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - guided_choice: Optional[List[str]] = None, - prompt_logprobs: Optional[int] = None, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, ) -> OpenAICompletion: - provider_model_id = self.get_provider_model_id(model) + provider_model_id = await self._get_provider_model_id(model) params = await prepare_openai_completion_params( model=provider_model_id, @@ -324,30 +336,30 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): async def openai_chat_completion( self, model: str, - messages: List[OpenAIMessageParam], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: - provider_model_id = self.get_provider_model_id(model) + messages: list[OpenAIMessageParam], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: + provider_model_id = await self._get_provider_model_id(model) params = await prepare_openai_completion_params( model=provider_model_id, @@ -379,3 +391,44 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): return await self._get_client(provider_model_id).chat.completions.create(**params) except APIConnectionError as e: raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e + + async def register_model(self, model: Model) -> Model: + """ + Allow non-llama model registration. + + Non-llama model registration: API Catalogue models, post-training models, etc. + client = LlamaStackAsLibraryClient("nvidia") + client.models.register( + model_id="mistralai/mixtral-8x7b-instruct-v0.1", + model_type=ModelType.llm, + provider_id="nvidia", + provider_model_id="mistralai/mixtral-8x7b-instruct-v0.1" + ) + + NOTE: Only supports models endpoints compatible with AsyncOpenAI base_url format. + """ + if model.model_type == ModelType.embedding: + # embedding models are always registered by their provider model id and does not need to be mapped to a llama model + provider_resource_id = model.provider_resource_id + else: + provider_resource_id = self.get_provider_model_id(model.provider_resource_id) + + if provider_resource_id: + model.provider_resource_id = provider_resource_id + else: + llama_model = model.metadata.get("llama_model") + existing_llama_model = self.get_llama_model(model.provider_resource_id) + if existing_llama_model: + if existing_llama_model != llama_model: + raise ValueError( + f"Provider model id '{model.provider_resource_id}' is already registered to a different llama model: '{existing_llama_model}'" + ) + else: + # not llama model + if llama_model in ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR: + self.provider_id_to_llama_model_map[model.provider_resource_id] = ( + ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[llama_model] + ) + else: + self.alias_to_provider_id_map[model.provider_model_id] = model.provider_model_id + return model diff --git a/llama_stack/providers/remote/inference/nvidia/openai_utils.py b/llama_stack/providers/remote/inference/nvidia/openai_utils.py index 3f2769b26..0b0d7fcf3 100644 --- a/llama_stack/providers/remote/inference/nvidia/openai_utils.py +++ b/llama_stack/providers/remote/inference/nvidia/openai_utils.py @@ -5,7 +5,8 @@ # the root directory of this source tree. import warnings -from typing import Any, AsyncGenerator, Dict, List, Optional +from collections.abc import AsyncGenerator +from typing import Any from openai import AsyncStream from openai.types.chat.chat_completion import ( @@ -64,7 +65,7 @@ async def convert_chat_completion_request( ) nvext = {} - payload: Dict[str, Any] = dict( + payload: dict[str, Any] = dict( model=request.model, messages=[await convert_message_to_openai_dict_new(message) for message in request.messages], stream=request.stream, @@ -137,7 +138,7 @@ def convert_completion_request( # logprobs.top_k -> logprobs nvext = {} - payload: Dict[str, Any] = dict( + payload: dict[str, Any] = dict( model=request.model, prompt=request.content, stream=request.stream, @@ -176,8 +177,8 @@ def convert_completion_request( def _convert_openai_completion_logprobs( - logprobs: Optional[OpenAICompletionLogprobs], -) -> Optional[List[TokenLogProbs]]: + logprobs: OpenAICompletionLogprobs | None, +) -> list[TokenLogProbs] | None: """ Convert an OpenAI CompletionLogprobs into a list of TokenLogProbs. """ diff --git a/llama_stack/providers/remote/inference/nvidia/utils.py b/llama_stack/providers/remote/inference/nvidia/utils.py index 7d3f3f27e..74019999e 100644 --- a/llama_stack/providers/remote/inference/nvidia/utils.py +++ b/llama_stack/providers/remote/inference/nvidia/utils.py @@ -5,7 +5,6 @@ # the root directory of this source tree. import logging -from typing import Tuple import httpx @@ -18,7 +17,7 @@ def _is_nvidia_hosted(config: NVIDIAConfig) -> bool: return "integrate.api.nvidia.com" in config.url -async def _get_health(url: str) -> Tuple[bool, bool]: +async def _get_health(url: str) -> tuple[bool, bool]: """ Query {url}/v1/health/{live,ready} to check if the server is running and ready diff --git a/llama_stack/providers/remote/inference/ollama/config.py b/llama_stack/providers/remote/inference/ollama/config.py index a5a4d48ab..0e4aef0e1 100644 --- a/llama_stack/providers/remote/inference/ollama/config.py +++ b/llama_stack/providers/remote/inference/ollama/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -15,5 +15,5 @@ class OllamaImplConfig(BaseModel): url: str = DEFAULT_OLLAMA_URL @classmethod - def sample_run_config(cls, url: str = "${env.OLLAMA_URL:http://localhost:11434}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, url: str = "${env.OLLAMA_URL:http://localhost:11434}", **kwargs) -> dict[str, Any]: return {"url": url} diff --git a/llama_stack/providers/remote/inference/ollama/ollama.py b/llama_stack/providers/remote/inference/ollama/ollama.py index cdfe7b568..0cf63097b 100644 --- a/llama_stack/providers/remote/inference/ollama/ollama.py +++ b/llama_stack/providers/remote/inference/ollama/ollama.py @@ -5,10 +5,11 @@ # the root directory of this source tree. -from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union +from collections.abc import AsyncGenerator, AsyncIterator +from typing import Any import httpx -from ollama import AsyncClient +from ollama import AsyncClient # type: ignore[attr-defined] from openai import AsyncOpenAI from llama_stack.apis.common.content_types import ( @@ -130,10 +131,10 @@ class OllamaInferenceAdapter( self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]: if sampling_params is None: sampling_params = SamplingParams() @@ -188,15 +189,15 @@ class OllamaInferenceAdapter( async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages: list[Message], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> ChatCompletionResponse | AsyncGenerator[ChatCompletionResponseStreamChunk, None]: if sampling_params is None: sampling_params = SamplingParams() @@ -216,7 +217,7 @@ class OllamaInferenceAdapter( else: return await self._nonstream_chat_completion(request) - async def _get_params(self, request: Union[ChatCompletionRequest, CompletionRequest]) -> dict: + async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict: sampling_options = get_sampling_options(request.sampling_params) # This is needed since the Ollama API expects num_predict to be set # for early truncation instead of max_tokens. @@ -314,10 +315,10 @@ class OllamaInferenceAdapter( async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: model = await self._get_model(model_id) @@ -333,7 +334,10 @@ class OllamaInferenceAdapter( return EmbeddingsResponse(embeddings=embeddings) async def register_model(self, model: Model) -> Model: - model = await self.register_helper.register_model(model) + try: + model = await self.register_helper.register_model(model) + except ValueError: + pass # Ignore statically unknown model, will check live listing if model.model_type == ModelType.embedding: logger.info(f"Pulling embedding model `{model.provider_resource_id}` if necessary...") await self.client.pull(model.provider_resource_id) @@ -342,9 +346,12 @@ class OllamaInferenceAdapter( # - models not currently running are run by the ollama server as needed response = await self.client.list() available_models = [m["model"] for m in response["models"]] - if model.provider_resource_id not in available_models: + provider_resource_id = self.register_helper.get_provider_model_id(model.provider_resource_id) + if provider_resource_id is None: + provider_resource_id = model.provider_resource_id + if provider_resource_id not in available_models: available_models_latest = [m["model"].split(":latest")[0] for m in response["models"]] - if model.provider_resource_id in available_models_latest: + if provider_resource_id in available_models_latest: logger.warning( f"Imprecise provider resource id was used but 'latest' is available in Ollama - using '{model.provider_resource_id}:latest'" ) @@ -352,30 +359,31 @@ class OllamaInferenceAdapter( raise ValueError( f"Model '{model.provider_resource_id}' is not available in Ollama. Available models: {', '.join(available_models)}" ) + model.provider_resource_id = provider_resource_id return model async def openai_completion( self, model: str, - prompt: Union[str, List[str], List[int], List[List[int]]], - best_of: Optional[int] = None, - echo: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - presence_penalty: Optional[float] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - guided_choice: Optional[List[str]] = None, - prompt_logprobs: Optional[int] = None, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, ) -> OpenAICompletion: if not isinstance(prompt, str): raise ValueError("Ollama does not support non-string prompts for completion") @@ -409,30 +417,36 @@ class OllamaInferenceAdapter( async def openai_chat_completion( self, model: str, - messages: List[OpenAIMessageParam], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: + messages: list[OpenAIMessageParam], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: model_obj = await self._get_model(model) + + # ollama still makes tool calls even when tool_choice is "none" + # so we need to remove the tools in that case + if tool_choice == "none" and tools is not None: + tools = None + params = { k: v for k, v in { @@ -467,27 +481,27 @@ class OllamaInferenceAdapter( async def batch_completion( self, model_id: str, - content_batch: List[InterleavedContent], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + content_batch: list[InterleavedContent], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ): raise NotImplementedError("Batch completion is not supported for Ollama") async def batch_chat_completion( self, model_id: str, - messages_batch: List[List[Message]], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_config: Optional[ToolConfig] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + messages_batch: list[list[Message]], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_config: ToolConfig | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ): raise NotImplementedError("Batch chat completion is not supported for Ollama") -async def convert_message_to_openai_dict_for_ollama(message: Message) -> List[dict]: +async def convert_message_to_openai_dict_for_ollama(message: Message) -> list[dict]: async def _convert_content(content) -> dict: if isinstance(content, ImageContentItem): return { diff --git a/llama_stack/providers/remote/inference/openai/__init__.py b/llama_stack/providers/remote/inference/openai/__init__.py index 000a03d33..c245dbe10 100644 --- a/llama_stack/providers/remote/inference/openai/__init__.py +++ b/llama_stack/providers/remote/inference/openai/__init__.py @@ -4,15 +4,13 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Optional - from pydantic import BaseModel from .config import OpenAIConfig class OpenAIProviderDataValidator(BaseModel): - openai_api_key: Optional[str] = None + openai_api_key: str | None = None async def get_adapter_impl(config: OpenAIConfig, _deps): diff --git a/llama_stack/providers/remote/inference/openai/config.py b/llama_stack/providers/remote/inference/openai/config.py index 2b0cc2c10..17fb98831 100644 --- a/llama_stack/providers/remote/inference/openai/config.py +++ b/llama_stack/providers/remote/inference/openai/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -12,7 +12,7 @@ from llama_stack.schema_utils import json_schema_type class OpenAIProviderDataValidator(BaseModel): - openai_api_key: Optional[str] = Field( + openai_api_key: str | None = Field( default=None, description="API key for OpenAI models", ) @@ -20,13 +20,13 @@ class OpenAIProviderDataValidator(BaseModel): @json_schema_type class OpenAIConfig(BaseModel): - api_key: Optional[str] = Field( + api_key: str | None = Field( default=None, description="API key for OpenAI models", ) @classmethod - def sample_run_config(cls, api_key: str = "${env.OPENAI_API_KEY}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, api_key: str = "${env.OPENAI_API_KEY}", **kwargs) -> dict[str, Any]: return { "api_key": api_key, } diff --git a/llama_stack/providers/remote/inference/passthrough/config.py b/llama_stack/providers/remote/inference/passthrough/config.py index 46325e428..ce41495ce 100644 --- a/llama_stack/providers/remote/inference/passthrough/config.py +++ b/llama_stack/providers/remote/inference/passthrough/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field, SecretStr @@ -18,13 +18,13 @@ class PassthroughImplConfig(BaseModel): description="The URL for the passthrough endpoint", ) - api_key: Optional[SecretStr] = Field( + api_key: SecretStr | None = Field( default=None, description="API Key for the passthrouth endpoint", ) @classmethod - def sample_run_config(cls, **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs) -> dict[str, Any]: return { "url": "${env.PASSTHROUGH_URL}", "api_key": "${env.PASSTHROUGH_API_KEY}", diff --git a/llama_stack/providers/remote/inference/passthrough/passthrough.py b/llama_stack/providers/remote/inference/passthrough/passthrough.py index af05320b0..78ee52641 100644 --- a/llama_stack/providers/remote/inference/passthrough/passthrough.py +++ b/llama_stack/providers/remote/inference/passthrough/passthrough.py @@ -4,7 +4,8 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union +from collections.abc import AsyncGenerator, AsyncIterator +from typing import Any from llama_stack_client import AsyncLlamaStackClient @@ -93,10 +94,10 @@ class PassthroughInferenceAdapter(Inference): self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -123,15 +124,15 @@ class PassthroughInferenceAdapter(Inference): async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages: list[Message], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -165,7 +166,7 @@ class PassthroughInferenceAdapter(Inference): else: return await self._nonstream_chat_completion(json_params) - async def _nonstream_chat_completion(self, json_params: Dict[str, Any]) -> ChatCompletionResponse: + async def _nonstream_chat_completion(self, json_params: dict[str, Any]) -> ChatCompletionResponse: client = self._get_client() response = await client.inference.chat_completion(**json_params) @@ -178,7 +179,7 @@ class PassthroughInferenceAdapter(Inference): logprobs=response.logprobs, ) - async def _stream_chat_completion(self, json_params: Dict[str, Any]) -> AsyncGenerator: + async def _stream_chat_completion(self, json_params: dict[str, Any]) -> AsyncGenerator: client = self._get_client() stream_response = await client.inference.chat_completion(**json_params) @@ -193,10 +194,10 @@ class PassthroughInferenceAdapter(Inference): async def embeddings( self, model_id: str, - contents: List[InterleavedContent], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[InterleavedContent], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: client = self._get_client() model = await self.model_store.get_model(model_id) @@ -212,24 +213,24 @@ class PassthroughInferenceAdapter(Inference): async def openai_completion( self, model: str, - prompt: Union[str, List[str], List[int], List[List[int]]], - best_of: Optional[int] = None, - echo: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - presence_penalty: Optional[float] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - guided_choice: Optional[List[str]] = None, - prompt_logprobs: Optional[int] = None, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, ) -> OpenAICompletion: client = self._get_client() model_obj = await self.model_store.get_model(model) @@ -261,29 +262,29 @@ class PassthroughInferenceAdapter(Inference): async def openai_chat_completion( self, model: str, - messages: List[OpenAIMessageParam], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: + messages: list[OpenAIMessageParam], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: client = self._get_client() model_obj = await self.model_store.get_model(model) @@ -315,7 +316,7 @@ class PassthroughInferenceAdapter(Inference): return await client.inference.openai_chat_completion(**params) - def cast_value_to_json_dict(self, request_params: Dict[str, Any]) -> Dict[str, Any]: + def cast_value_to_json_dict(self, request_params: dict[str, Any]) -> dict[str, Any]: json_params = {} for key, value in request_params.items(): json_input = convert_pydantic_to_json_value(value) diff --git a/llama_stack/providers/remote/inference/runpod/config.py b/llama_stack/providers/remote/inference/runpod/config.py index 377a7fe6a..e3913dc35 100644 --- a/llama_stack/providers/remote/inference/runpod/config.py +++ b/llama_stack/providers/remote/inference/runpod/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -13,17 +13,17 @@ from llama_stack.schema_utils import json_schema_type @json_schema_type class RunpodImplConfig(BaseModel): - url: Optional[str] = Field( + url: str | None = Field( default=None, description="The URL for the Runpod model serving endpoint", ) - api_token: Optional[str] = Field( + api_token: str | None = Field( default=None, description="The API token", ) @classmethod - def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs: Any) -> dict[str, Any]: return { "url": "${env.RUNPOD_URL:}", "api_token": "${env.RUNPOD_API_TOKEN:}", diff --git a/llama_stack/providers/remote/inference/runpod/runpod.py b/llama_stack/providers/remote/inference/runpod/runpod.py index 72cbead9b..2706aa15e 100644 --- a/llama_stack/providers/remote/inference/runpod/runpod.py +++ b/llama_stack/providers/remote/inference/runpod/runpod.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import AsyncGenerator +from collections.abc import AsyncGenerator from openai import OpenAI diff --git a/llama_stack/providers/remote/inference/sambanova/config.py b/llama_stack/providers/remote/inference/sambanova/config.py index 6b31af2f0..abbf9430f 100644 --- a/llama_stack/providers/remote/inference/sambanova/config.py +++ b/llama_stack/providers/remote/inference/sambanova/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field, SecretStr @@ -12,7 +12,7 @@ from llama_stack.schema_utils import json_schema_type class SambaNovaProviderDataValidator(BaseModel): - sambanova_api_key: Optional[str] = Field( + sambanova_api_key: str | None = Field( default=None, description="Sambanova Cloud API key", ) @@ -24,13 +24,13 @@ class SambaNovaImplConfig(BaseModel): default="https://api.sambanova.ai/v1", description="The URL for the SambaNova AI server", ) - api_key: Optional[SecretStr] = Field( + api_key: SecretStr | None = Field( default=None, description="The SambaNova cloud API Key", ) @classmethod - def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY}", **kwargs) -> dict[str, Any]: return { "url": "https://api.sambanova.ai/v1", "api_key": api_key, diff --git a/llama_stack/providers/remote/inference/sambanova/sambanova.py b/llama_stack/providers/remote/inference/sambanova/sambanova.py index d7387c9ba..d182aa1dc 100644 --- a/llama_stack/providers/remote/inference/sambanova/sambanova.py +++ b/llama_stack/providers/remote/inference/sambanova/sambanova.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import json -from typing import Dict, Iterable, List, Union +from collections.abc import Iterable from openai.types.chat import ( ChatCompletionAssistantMessageParam as OpenAIChatCompletionAssistantMessage, @@ -72,7 +72,7 @@ logger = get_logger(name=__name__, category="inference") async def convert_message_to_openai_dict_with_b64_images( - message: Message | Dict, + message: Message | dict, ) -> OpenAIChatCompletionMessage: """ Convert a Message to an OpenAI API-compatible dictionary. @@ -101,10 +101,10 @@ async def convert_message_to_openai_dict_with_b64_images( # List[...] -> List[...] async def _convert_message_content( content: InterleavedContent, - ) -> Union[str, Iterable[OpenAIChatCompletionContentPartParam]]: + ) -> str | Iterable[OpenAIChatCompletionContentPartParam]: async def impl( content_: InterleavedContent, - ) -> Union[str, OpenAIChatCompletionContentPartParam, List[OpenAIChatCompletionContentPartParam]]: + ) -> str | OpenAIChatCompletionContentPartParam | list[OpenAIChatCompletionContentPartParam]: # Llama Stack and OpenAI spec match for str and text input if isinstance(content_, str): return content_ diff --git a/llama_stack/providers/remote/inference/sambanova_openai_compat/config.py b/llama_stack/providers/remote/inference/sambanova_openai_compat/config.py index b792cb6e7..072fa85d1 100644 --- a/llama_stack/providers/remote/inference/sambanova_openai_compat/config.py +++ b/llama_stack/providers/remote/inference/sambanova_openai_compat/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -12,7 +12,7 @@ from llama_stack.schema_utils import json_schema_type class SambaNovaProviderDataValidator(BaseModel): - sambanova_api_key: Optional[str] = Field( + sambanova_api_key: str | None = Field( default=None, description="API key for SambaNova models", ) @@ -20,7 +20,7 @@ class SambaNovaProviderDataValidator(BaseModel): @json_schema_type class SambaNovaCompatConfig(BaseModel): - api_key: Optional[str] = Field( + api_key: str | None = Field( default=None, description="The SambaNova API key", ) @@ -31,7 +31,7 @@ class SambaNovaCompatConfig(BaseModel): ) @classmethod - def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY}", **kwargs) -> dict[str, Any]: return { "openai_compat_api_base": "https://api.sambanova.ai/v1", "api_key": api_key, diff --git a/llama_stack/providers/remote/inference/tgi/__init__.py b/llama_stack/providers/remote/inference/tgi/__init__.py index 834e51324..51614f1a6 100644 --- a/llama_stack/providers/remote/inference/tgi/__init__.py +++ b/llama_stack/providers/remote/inference/tgi/__init__.py @@ -4,13 +4,11 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Union - from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig async def get_adapter_impl( - config: Union[InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig], + config: InferenceAPIImplConfig | InferenceEndpointImplConfig | TGIImplConfig, _deps, ): from .tgi import InferenceAPIAdapter, InferenceEndpointAdapter, TGIAdapter diff --git a/llama_stack/providers/remote/inference/tgi/config.py b/llama_stack/providers/remote/inference/tgi/config.py index 6ad663662..3d632c9d8 100644 --- a/llama_stack/providers/remote/inference/tgi/config.py +++ b/llama_stack/providers/remote/inference/tgi/config.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Optional from pydantic import BaseModel, Field, SecretStr @@ -29,7 +28,7 @@ class InferenceEndpointImplConfig(BaseModel): endpoint_name: str = Field( description="The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided.", ) - api_token: Optional[SecretStr] = Field( + api_token: SecretStr | None = Field( default=None, description="Your Hugging Face user access token (will default to locally saved token if not provided)", ) @@ -52,7 +51,7 @@ class InferenceAPIImplConfig(BaseModel): huggingface_repo: str = Field( description="The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct')", ) - api_token: Optional[SecretStr] = Field( + api_token: SecretStr | None = Field( default=None, description="Your Hugging Face user access token (will default to locally saved token if not provided)", ) diff --git a/llama_stack/providers/remote/inference/tgi/tgi.py b/llama_stack/providers/remote/inference/tgi/tgi.py index 4ee386a15..8f6666462 100644 --- a/llama_stack/providers/remote/inference/tgi/tgi.py +++ b/llama_stack/providers/remote/inference/tgi/tgi.py @@ -6,7 +6,7 @@ import logging -from typing import AsyncGenerator, List, Optional +from collections.abc import AsyncGenerator from huggingface_hub import AsyncInferenceClient, HfApi @@ -105,10 +105,10 @@ class _HfAdapter( self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -134,7 +134,7 @@ class _HfAdapter( def _build_options( self, - sampling_params: Optional[SamplingParams] = None, + sampling_params: SamplingParams | None = None, fmt: ResponseFormat = None, ): options = get_sampling_options(sampling_params) @@ -209,15 +209,15 @@ class _HfAdapter( async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages: list[Message], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -284,10 +284,10 @@ class _HfAdapter( async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: raise NotImplementedError() diff --git a/llama_stack/providers/remote/inference/together/config.py b/llama_stack/providers/remote/inference/together/config.py index fa7c45c9f..5c7f60519 100644 --- a/llama_stack/providers/remote/inference/together/config.py +++ b/llama_stack/providers/remote/inference/together/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field, SecretStr @@ -17,13 +17,13 @@ class TogetherImplConfig(BaseModel): default="https://api.together.xyz/v1", description="The URL for the Together AI server", ) - api_key: Optional[SecretStr] = Field( + api_key: SecretStr | None = Field( default=None, description="The Together AI API Key", ) @classmethod - def sample_run_config(cls, **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs) -> dict[str, Any]: return { "url": "https://api.together.xyz/v1", "api_key": "${env.TOGETHER_API_KEY:}", diff --git a/llama_stack/providers/remote/inference/together/together.py b/llama_stack/providers/remote/inference/together/together.py index 001e6aac4..562e6e0ff 100644 --- a/llama_stack/providers/remote/inference/together/together.py +++ b/llama_stack/providers/remote/inference/together/together.py @@ -4,7 +4,8 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union +from collections.abc import AsyncGenerator, AsyncIterator +from typing import Any from openai import AsyncOpenAI from together import AsyncTogether @@ -76,17 +77,20 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi async def shutdown(self) -> None: if self._client: - await self._client.close() + # Together client has no close method, so just set to None self._client = None + if self._openai_client: + await self._openai_client.close() + self._openai_client = None async def completion( self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -144,8 +148,8 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi def _build_options( self, - sampling_params: Optional[SamplingParams], - logprobs: Optional[LogProbConfig], + sampling_params: SamplingParams | None, + logprobs: LogProbConfig | None, fmt: ResponseFormat, ) -> dict: options = get_sampling_options(sampling_params) @@ -172,15 +176,15 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages: list[Message], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -221,7 +225,7 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi async for chunk in process_chat_completion_stream_response(stream, request): yield chunk - async def _get_params(self, request: Union[ChatCompletionRequest, CompletionRequest]) -> dict: + async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict: input_dict = {} media_present = request_has_media(request) llama_model = self.get_llama_model(request.model) @@ -246,10 +250,10 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: model = await self.model_store.get_model(model_id) assert all(not content_has_media(content) for content in contents), ( @@ -266,24 +270,24 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi async def openai_completion( self, model: str, - prompt: Union[str, List[str], List[int], List[List[int]]], - best_of: Optional[int] = None, - echo: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - presence_penalty: Optional[float] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - guided_choice: Optional[List[str]] = None, - prompt_logprobs: Optional[int] = None, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, ) -> OpenAICompletion: model_obj = await self.model_store.get_model(model) params = await prepare_openai_completion_params( @@ -310,29 +314,29 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi async def openai_chat_completion( self, model: str, - messages: List[OpenAIMessageParam], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: + messages: list[OpenAIMessageParam], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: model_obj = await self.model_store.get_model(model) params = await prepare_openai_completion_params( model=model_obj.provider_resource_id, @@ -359,7 +363,7 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi top_p=top_p, user=user, ) - if params.get("stream", True): + if params.get("stream", False): return self._stream_openai_chat_completion(params) return await self._get_openai_client().chat.completions.create(**params) # type: ignore diff --git a/llama_stack/providers/remote/inference/together_openai_compat/config.py b/llama_stack/providers/remote/inference/together_openai_compat/config.py index 120adbed9..0c6d4f748 100644 --- a/llama_stack/providers/remote/inference/together_openai_compat/config.py +++ b/llama_stack/providers/remote/inference/together_openai_compat/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -12,7 +12,7 @@ from llama_stack.schema_utils import json_schema_type class TogetherProviderDataValidator(BaseModel): - together_api_key: Optional[str] = Field( + together_api_key: str | None = Field( default=None, description="API key for Together models", ) @@ -20,7 +20,7 @@ class TogetherProviderDataValidator(BaseModel): @json_schema_type class TogetherCompatConfig(BaseModel): - api_key: Optional[str] = Field( + api_key: str | None = Field( default=None, description="The Together API key", ) @@ -31,7 +31,7 @@ class TogetherCompatConfig(BaseModel): ) @classmethod - def sample_run_config(cls, api_key: str = "${env.TOGETHER_API_KEY}", **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, api_key: str = "${env.TOGETHER_API_KEY}", **kwargs) -> dict[str, Any]: return { "openai_compat_api_base": "https://api.together.xyz/v1", "api_key": api_key, diff --git a/llama_stack/providers/remote/inference/vllm/config.py b/llama_stack/providers/remote/inference/vllm/config.py index 762cffde3..8530594b6 100644 --- a/llama_stack/providers/remote/inference/vllm/config.py +++ b/llama_stack/providers/remote/inference/vllm/config.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Optional from pydantic import BaseModel, Field @@ -13,7 +12,7 @@ from llama_stack.schema_utils import json_schema_type @json_schema_type class VLLMInferenceAdapterConfig(BaseModel): - url: Optional[str] = Field( + url: str | None = Field( default=None, description="The URL for the vLLM model serving endpoint", ) @@ -21,7 +20,7 @@ class VLLMInferenceAdapterConfig(BaseModel): default=4096, description="Maximum number of tokens to generate.", ) - api_token: Optional[str] = Field( + api_token: str | None = Field( default="fake", description="The API token", ) diff --git a/llama_stack/providers/remote/inference/vllm/vllm.py b/llama_stack/providers/remote/inference/vllm/vllm.py index 2b9eae1e9..addf2d35b 100644 --- a/llama_stack/providers/remote/inference/vllm/vllm.py +++ b/llama_stack/providers/remote/inference/vllm/vllm.py @@ -5,7 +5,8 @@ # the root directory of this source tree. import json import logging -from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union +from collections.abc import AsyncGenerator, AsyncIterator +from typing import Any import httpx from openai import AsyncOpenAI @@ -94,7 +95,7 @@ def build_hf_repo_model_entries(): def _convert_to_vllm_tool_calls_in_response( tool_calls, -) -> List[ToolCall]: +) -> list[ToolCall]: if not tool_calls: return [] @@ -109,7 +110,7 @@ def _convert_to_vllm_tool_calls_in_response( ] -def _convert_to_vllm_tools_in_request(tools: List[ToolDefinition]) -> List[dict]: +def _convert_to_vllm_tools_in_request(tools: list[ToolDefinition]) -> list[dict]: compat_tools = [] for tool in tools: @@ -231,12 +232,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): self.client = None async def initialize(self) -> None: - log.info(f"Initializing VLLM client with base_url={self.config.url}") - self.client = AsyncOpenAI( - base_url=self.config.url, - api_key=self.config.api_token, - http_client=None if self.config.tls_verify else httpx.AsyncClient(verify=False), - ) + pass async def shutdown(self) -> None: pass @@ -249,15 +245,30 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): raise ValueError("Model store not set") return await self.model_store.get_model(model_id) + def _lazy_initialize_client(self): + if self.client is not None: + return + + log.info(f"Initializing vLLM client with base_url={self.config.url}") + self.client = self._create_client() + + def _create_client(self): + return AsyncOpenAI( + base_url=self.config.url, + api_key=self.config.api_token, + http_client=None if self.config.tls_verify else httpx.AsyncClient(verify=False), + ) + async def completion( self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]: + self._lazy_initialize_client() if sampling_params is None: sampling_params = SamplingParams() model = await self._get_model(model_id) @@ -277,16 +288,17 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + messages: list[Message], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> ChatCompletionResponse | AsyncGenerator[ChatCompletionResponseStreamChunk, None]: + self._lazy_initialize_client() if sampling_params is None: sampling_params = SamplingParams() model = await self._get_model(model_id) @@ -357,9 +369,15 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): yield chunk async def register_model(self, model: Model) -> Model: - assert self.client is not None - model = await self.register_helper.register_model(model) - res = await self.client.models.list() + # register_model is called during Llama Stack initialization, hence we cannot init self.client if not initialized yet. + # self.client should only be created after the initialization is complete to avoid asyncio cross-context errors. + # Changing this may lead to unpredictable behavior. + client = self._create_client() if self.client is None else self.client + try: + model = await self.register_helper.register_model(model) + except ValueError: + pass # Ignore statically unknown model, will check live listing + res = await client.models.list() available_models = [m.id async for m in res] if model.provider_resource_id not in available_models: raise ValueError( @@ -368,13 +386,14 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): ) return model - async def _get_params(self, request: Union[ChatCompletionRequest, CompletionRequest]) -> dict: + async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict: options = get_sampling_options(request.sampling_params) if "max_tokens" not in options: options["max_tokens"] = self.config.max_tokens input_dict: dict[str, Any] = {} - if isinstance(request, ChatCompletionRequest) and request.tools is not None: + # Only include the 'tools' param if there is any. It can break things if an empty list is sent to the vLLM. + if isinstance(request, ChatCompletionRequest) and request.tools: input_dict = {"tools": _convert_to_vllm_tools_in_request(request.tools)} if isinstance(request, ChatCompletionRequest): @@ -404,11 +423,12 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: + self._lazy_initialize_client() assert self.client is not None model = await self._get_model(model_id) @@ -429,28 +449,29 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): async def openai_completion( self, model: str, - prompt: Union[str, List[str], List[int], List[List[int]]], - best_of: Optional[int] = None, - echo: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - presence_penalty: Optional[float] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - guided_choice: Optional[List[str]] = None, - prompt_logprobs: Optional[int] = None, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, ) -> OpenAICompletion: + self._lazy_initialize_client() model_obj = await self._get_model(model) - extra_body: Dict[str, Any] = {} + extra_body: dict[str, Any] = {} if prompt_logprobs is not None and prompt_logprobs >= 0: extra_body["prompt_logprobs"] = prompt_logprobs if guided_choice: @@ -481,29 +502,30 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): async def openai_chat_completion( self, model: str, - messages: List[OpenAIMessageParam], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: + messages: list[OpenAIMessageParam], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: + self._lazy_initialize_client() model_obj = await self._get_model(model) params = await prepare_openai_completion_params( model=model_obj.provider_resource_id, @@ -535,21 +557,21 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): async def batch_completion( self, model_id: str, - content_batch: List[InterleavedContent], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + content_batch: list[InterleavedContent], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ): raise NotImplementedError("Batch completion is not supported for Ollama") async def batch_chat_completion( self, model_id: str, - messages_batch: List[List[Message]], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_config: Optional[ToolConfig] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + messages_batch: list[list[Message]], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_config: ToolConfig | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ): raise NotImplementedError("Batch chat completion is not supported for Ollama") diff --git a/llama_stack/providers/remote/inference/watsonx/__init__.py b/llama_stack/providers/remote/inference/watsonx/__init__.py new file mode 100644 index 000000000..e59e873b6 --- /dev/null +++ b/llama_stack/providers/remote/inference/watsonx/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from llama_stack.apis.inference import Inference + +from .config import WatsonXConfig + + +async def get_adapter_impl(config: WatsonXConfig, _deps) -> Inference: + # import dynamically so `llama stack build` does not fail due to missing dependencies + from .watsonx import WatsonXInferenceAdapter + + if not isinstance(config, WatsonXConfig): + raise RuntimeError(f"Unexpected config type: {type(config)}") + adapter = WatsonXInferenceAdapter(config) + return adapter + + +__all__ = ["get_adapter_impl", "WatsonXConfig"] diff --git a/llama_stack/providers/remote/inference/watsonx/config.py b/llama_stack/providers/remote/inference/watsonx/config.py new file mode 100644 index 000000000..5eda9c5c0 --- /dev/null +++ b/llama_stack/providers/remote/inference/watsonx/config.py @@ -0,0 +1,46 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import os +from typing import Any + +from pydantic import BaseModel, Field, SecretStr + +from llama_stack.schema_utils import json_schema_type + + +class WatsonXProviderDataValidator(BaseModel): + url: str + api_key: str + project_id: str + + +@json_schema_type +class WatsonXConfig(BaseModel): + url: str = Field( + default_factory=lambda: os.getenv("WATSONX_BASE_URL", "https://us-south.ml.cloud.ibm.com"), + description="A base url for accessing the watsonx.ai", + ) + api_key: SecretStr | None = Field( + default_factory=lambda: os.getenv("WATSONX_API_KEY"), + description="The watsonx API key, only needed of using the hosted service", + ) + project_id: str | None = Field( + default_factory=lambda: os.getenv("WATSONX_PROJECT_ID"), + description="The Project ID key, only needed of using the hosted service", + ) + timeout: int = Field( + default=60, + description="Timeout for the HTTP requests", + ) + + @classmethod + def sample_run_config(cls, **kwargs) -> dict[str, Any]: + return { + "url": "${env.WATSONX_BASE_URL:https://us-south.ml.cloud.ibm.com}", + "api_key": "${env.WATSONX_API_KEY:}", + "project_id": "${env.WATSONX_PROJECT_ID:}", + } diff --git a/llama_stack/providers/remote/inference/watsonx/models.py b/llama_stack/providers/remote/inference/watsonx/models.py new file mode 100644 index 000000000..d98f0510a --- /dev/null +++ b/llama_stack/providers/remote/inference/watsonx/models.py @@ -0,0 +1,47 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from llama_stack.models.llama.sku_types import CoreModelId +from llama_stack.providers.utils.inference.model_registry import build_hf_repo_model_entry + +MODEL_ENTRIES = [ + build_hf_repo_model_entry( + "meta-llama/llama-3-3-70b-instruct", + CoreModelId.llama3_3_70b_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/llama-2-13b-chat", + CoreModelId.llama2_13b.value, + ), + build_hf_repo_model_entry( + "meta-llama/llama-3-1-70b-instruct", + CoreModelId.llama3_1_70b_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/llama-3-1-8b-instruct", + CoreModelId.llama3_1_8b_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/llama-3-2-11b-vision-instruct", + CoreModelId.llama3_2_11b_vision_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/llama-3-2-1b-instruct", + CoreModelId.llama3_2_1b_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/llama-3-2-3b-instruct", + CoreModelId.llama3_2_3b_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/llama-3-2-90b-vision-instruct", + CoreModelId.llama3_2_90b_vision_instruct.value, + ), + build_hf_repo_model_entry( + "meta-llama/llama-guard-3-11b-vision", + CoreModelId.llama_guard_3_11b_vision.value, + ), +] diff --git a/llama_stack/providers/remote/inference/watsonx/watsonx.py b/llama_stack/providers/remote/inference/watsonx/watsonx.py new file mode 100644 index 000000000..c1299e11f --- /dev/null +++ b/llama_stack/providers/remote/inference/watsonx/watsonx.py @@ -0,0 +1,379 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from collections.abc import AsyncGenerator, AsyncIterator +from typing import Any + +from ibm_watson_machine_learning.foundation_models import Model +from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams +from openai import AsyncOpenAI + +from llama_stack.apis.common.content_types import InterleavedContent, InterleavedContentItem +from llama_stack.apis.inference import ( + ChatCompletionRequest, + ChatCompletionResponse, + CompletionRequest, + EmbeddingsResponse, + EmbeddingTaskType, + Inference, + LogProbConfig, + Message, + ResponseFormat, + SamplingParams, + TextTruncation, + ToolChoice, + ToolConfig, + ToolDefinition, + ToolPromptFormat, +) +from llama_stack.apis.inference.inference import ( + GreedySamplingStrategy, + OpenAIChatCompletion, + OpenAIChatCompletionChunk, + OpenAICompletion, + OpenAIMessageParam, + OpenAIResponseFormatParam, + TopKSamplingStrategy, + TopPSamplingStrategy, +) +from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper +from llama_stack.providers.utils.inference.openai_compat import ( + OpenAICompatCompletionChoice, + OpenAICompatCompletionResponse, + prepare_openai_completion_params, + process_chat_completion_response, + process_chat_completion_stream_response, + process_completion_response, + process_completion_stream_response, +) +from llama_stack.providers.utils.inference.prompt_adapter import ( + chat_completion_request_to_prompt, + completion_request_to_prompt, + request_has_media, +) + +from . import WatsonXConfig +from .models import MODEL_ENTRIES + + +class WatsonXInferenceAdapter(Inference, ModelRegistryHelper): + def __init__(self, config: WatsonXConfig) -> None: + ModelRegistryHelper.__init__(self, MODEL_ENTRIES) + + print(f"Initializing watsonx InferenceAdapter({config.url})...") + + self._config = config + + self._project_id = self._config.project_id + + async def initialize(self) -> None: + pass + + async def shutdown(self) -> None: + pass + + async def completion( + self, + model_id: str, + content: InterleavedContent, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + ) -> AsyncGenerator: + if sampling_params is None: + sampling_params = SamplingParams() + model = await self.model_store.get_model(model_id) + request = CompletionRequest( + model=model.provider_resource_id, + content=content, + sampling_params=sampling_params, + response_format=response_format, + stream=stream, + logprobs=logprobs, + ) + if stream: + return self._stream_completion(request) + else: + return await self._nonstream_completion(request) + + def _get_client(self, model_id) -> Model: + config_api_key = self._config.api_key.get_secret_value() if self._config.api_key else None + config_url = self._config.url + project_id = self._config.project_id + credentials = {"url": config_url, "apikey": config_api_key} + + return Model(model_id=model_id, credentials=credentials, project_id=project_id) + + def _get_openai_client(self) -> AsyncOpenAI: + if not self._openai_client: + self._openai_client = AsyncOpenAI( + base_url=f"{self._config.url}/openai/v1", + api_key=self._config.api_key, + ) + return self._openai_client + + async def _nonstream_completion(self, request: CompletionRequest) -> ChatCompletionResponse: + params = await self._get_params(request) + r = self._get_client(request.model).generate(**params) + choices = [] + if "results" in r: + for result in r["results"]: + choice = OpenAICompatCompletionChoice( + finish_reason=result["stop_reason"] if result["stop_reason"] else None, + text=result["generated_text"], + ) + choices.append(choice) + response = OpenAICompatCompletionResponse( + choices=choices, + ) + return process_completion_response(response) + + async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator: + params = await self._get_params(request) + + async def _generate_and_convert_to_openai_compat(): + s = self._get_client(request.model).generate_text_stream(**params) + for chunk in s: + choice = OpenAICompatCompletionChoice( + finish_reason=None, + text=chunk, + ) + yield OpenAICompatCompletionResponse( + choices=[choice], + ) + + stream = _generate_and_convert_to_openai_compat() + async for chunk in process_completion_stream_response(stream): + yield chunk + + async def chat_completion( + self, + model_id: str, + messages: list[Message], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, + ) -> AsyncGenerator: + if sampling_params is None: + sampling_params = SamplingParams() + model = await self.model_store.get_model(model_id) + request = ChatCompletionRequest( + model=model.provider_resource_id, + messages=messages, + sampling_params=sampling_params, + tools=tools or [], + response_format=response_format, + stream=stream, + logprobs=logprobs, + tool_config=tool_config, + ) + + if stream: + return self._stream_chat_completion(request) + else: + return await self._nonstream_chat_completion(request) + + async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse: + params = await self._get_params(request) + r = self._get_client(request.model).generate(**params) + choices = [] + if "results" in r: + for result in r["results"]: + choice = OpenAICompatCompletionChoice( + finish_reason=result["stop_reason"] if result["stop_reason"] else None, + text=result["generated_text"], + ) + choices.append(choice) + response = OpenAICompatCompletionResponse( + choices=choices, + ) + return process_chat_completion_response(response, request) + + async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator: + params = await self._get_params(request) + model_id = request.model + + # if we shift to TogetherAsyncClient, we won't need this wrapper + async def _to_async_generator(): + s = self._get_client(model_id).generate_text_stream(**params) + for chunk in s: + choice = OpenAICompatCompletionChoice( + finish_reason=None, + text=chunk, + ) + yield OpenAICompatCompletionResponse( + choices=[choice], + ) + + stream = _to_async_generator() + async for chunk in process_chat_completion_stream_response(stream, request): + yield chunk + + async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict: + input_dict = {"params": {}} + media_present = request_has_media(request) + llama_model = self.get_llama_model(request.model) + if isinstance(request, ChatCompletionRequest): + input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model) + else: + assert not media_present, "Together does not support media for Completion requests" + input_dict["prompt"] = await completion_request_to_prompt(request) + if request.sampling_params: + if request.sampling_params.strategy: + input_dict["params"][GenParams.DECODING_METHOD] = request.sampling_params.strategy.type + if request.sampling_params.max_tokens: + input_dict["params"][GenParams.MAX_NEW_TOKENS] = request.sampling_params.max_tokens + if request.sampling_params.repetition_penalty: + input_dict["params"][GenParams.REPETITION_PENALTY] = request.sampling_params.repetition_penalty + + if isinstance(request.sampling_params.strategy, TopPSamplingStrategy): + input_dict["params"][GenParams.TOP_P] = request.sampling_params.strategy.top_p + input_dict["params"][GenParams.TEMPERATURE] = request.sampling_params.strategy.temperature + if isinstance(request.sampling_params.strategy, TopKSamplingStrategy): + input_dict["params"][GenParams.TOP_K] = request.sampling_params.strategy.top_k + if isinstance(request.sampling_params.strategy, GreedySamplingStrategy): + input_dict["params"][GenParams.TEMPERATURE] = 0.0 + + input_dict["params"][GenParams.STOP_SEQUENCES] = ["<|endoftext|>"] + + params = { + **input_dict, + } + return params + + async def embeddings( + self, + model_id: str, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, + ) -> EmbeddingsResponse: + raise NotImplementedError("embedding is not supported for watsonx") + + async def openai_completion( + self, + model: str, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, + ) -> OpenAICompletion: + model_obj = await self.model_store.get_model(model) + params = await prepare_openai_completion_params( + model=model_obj.provider_resource_id, + prompt=prompt, + best_of=best_of, + echo=echo, + frequency_penalty=frequency_penalty, + logit_bias=logit_bias, + logprobs=logprobs, + max_tokens=max_tokens, + n=n, + presence_penalty=presence_penalty, + seed=seed, + stop=stop, + stream=stream, + stream_options=stream_options, + temperature=temperature, + top_p=top_p, + user=user, + ) + return await self._get_openai_client().completions.create(**params) # type: ignore + + async def openai_chat_completion( + self, + model: str, + messages: list[OpenAIMessageParam], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: + model_obj = await self.model_store.get_model(model) + params = await prepare_openai_completion_params( + model=model_obj.provider_resource_id, + messages=messages, + frequency_penalty=frequency_penalty, + function_call=function_call, + functions=functions, + logit_bias=logit_bias, + logprobs=logprobs, + max_completion_tokens=max_completion_tokens, + max_tokens=max_tokens, + n=n, + parallel_tool_calls=parallel_tool_calls, + presence_penalty=presence_penalty, + response_format=response_format, + seed=seed, + stop=stop, + stream=stream, + stream_options=stream_options, + temperature=temperature, + tool_choice=tool_choice, + tools=tools, + top_logprobs=top_logprobs, + top_p=top_p, + user=user, + ) + if params.get("stream", False): + return self._stream_openai_chat_completion(params) + return await self._get_openai_client().chat.completions.create(**params) # type: ignore + + async def _stream_openai_chat_completion(self, params: dict) -> AsyncGenerator: + # watsonx.ai sometimes adds usage data to the stream + include_usage = False + if params.get("stream_options", None): + include_usage = params["stream_options"].get("include_usage", False) + stream = await self._get_openai_client().chat.completions.create(**params) + + seen_finish_reason = False + async for chunk in stream: + # Final usage chunk with no choices that the user didn't request, so discard + if not include_usage and seen_finish_reason and len(chunk.choices) == 0: + break + yield chunk + for choice in chunk.choices: + if choice.finish_reason: + seen_finish_reason = True + break diff --git a/llama_stack/providers/remote/post_training/nvidia/README.md b/llama_stack/providers/remote/post_training/nvidia/README.md index 230587d66..3ef538d29 100644 --- a/llama_stack/providers/remote/post_training/nvidia/README.md +++ b/llama_stack/providers/remote/post_training/nvidia/README.md @@ -36,7 +36,6 @@ import os os.environ["NVIDIA_API_KEY"] = "your-api-key" os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test" -os.environ["NVIDIA_USER_ID"] = "llama-stack-user" os.environ["NVIDIA_DATASET_NAMESPACE"] = "default" os.environ["NVIDIA_PROJECT_ID"] = "test-project" os.environ["NVIDIA_OUTPUT_MODEL_DIR"] = "test-example-model@v1" @@ -125,6 +124,21 @@ client.post_training.job.cancel(job_uuid="your-job-id") ### Inference with the fine-tuned model +#### 1. Register the model + +```python +from llama_stack.apis.models import Model, ModelType + +client.models.register( + model_id="test-example-model@v1", + provider_id="nvidia", + provider_model_id="test-example-model@v1", + model_type=ModelType.llm, +) +``` + +#### 2. Inference with the fine-tuned model + ```python response = client.inference.completion( content="Complete the sentence using one word: Roses are red, violets are ", diff --git a/llama_stack/providers/remote/post_training/nvidia/config.py b/llama_stack/providers/remote/post_training/nvidia/config.py index 7b42c8bb0..fa08b6e3f 100644 --- a/llama_stack/providers/remote/post_training/nvidia/config.py +++ b/llama_stack/providers/remote/post_training/nvidia/config.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import os -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -15,23 +15,23 @@ from pydantic import BaseModel, Field class NvidiaPostTrainingConfig(BaseModel): """Configuration for NVIDIA Post Training implementation.""" - api_key: Optional[str] = Field( + api_key: str | None = Field( default_factory=lambda: os.getenv("NVIDIA_API_KEY"), description="The NVIDIA API key.", ) - dataset_namespace: Optional[str] = Field( + dataset_namespace: str | None = Field( default_factory=lambda: os.getenv("NVIDIA_DATASET_NAMESPACE", "default"), description="The NVIDIA dataset namespace.", ) - project_id: Optional[str] = Field( + project_id: str | None = Field( default_factory=lambda: os.getenv("NVIDIA_PROJECT_ID", "test-example-model@v1"), description="The NVIDIA project ID.", ) # ToDO: validate this, add default value - customizer_url: Optional[str] = Field( + customizer_url: str | None = Field( default_factory=lambda: os.getenv("NVIDIA_CUSTOMIZER_URL"), description="Base URL for the NeMo Customizer API", ) @@ -53,7 +53,7 @@ class NvidiaPostTrainingConfig(BaseModel): ) @classmethod - def sample_run_config(cls, **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs) -> dict[str, Any]: return { "api_key": "${env.NVIDIA_API_KEY:}", "dataset_namespace": "${env.NVIDIA_DATASET_NAMESPACE:default}", @@ -71,27 +71,27 @@ class SFTLoRADefaultConfig(BaseModel): n_epochs: int = 50 # NeMo customizer specific parameters - log_every_n_steps: Optional[int] = None + log_every_n_steps: int | None = None val_check_interval: float = 0.25 sequence_packing_enabled: bool = False weight_decay: float = 0.01 lr: float = 0.0001 # SFT specific parameters - hidden_dropout: Optional[float] = None - attention_dropout: Optional[float] = None - ffn_dropout: Optional[float] = None + hidden_dropout: float | None = None + attention_dropout: float | None = None + ffn_dropout: float | None = None # LoRA default parameters lora_adapter_dim: int = 8 - lora_adapter_dropout: Optional[float] = None + lora_adapter_dropout: float | None = None lora_alpha: int = 16 # Data config batch_size: int = 8 @classmethod - def sample_config(cls) -> Dict[str, Any]: + def sample_config(cls) -> dict[str, Any]: """Return a sample configuration for NVIDIA training.""" return { "n_epochs": 50, diff --git a/llama_stack/providers/remote/post_training/nvidia/models.py b/llama_stack/providers/remote/post_training/nvidia/models.py index 7c696ac20..6a28f8af8 100644 --- a/llama_stack/providers/remote/post_training/nvidia/models.py +++ b/llama_stack/providers/remote/post_training/nvidia/models.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List from llama_stack.models.llama.sku_types import CoreModelId from llama_stack.providers.utils.inference.model_registry import ( @@ -16,9 +15,13 @@ _MODEL_ENTRIES = [ build_hf_repo_model_entry( "meta/llama-3.1-8b-instruct", CoreModelId.llama3_1_8b_instruct.value, - ) + ), + build_hf_repo_model_entry( + "meta/llama-3.2-1b-instruct", + CoreModelId.llama3_2_1b_instruct.value, + ), ] -def get_model_entries() -> List[ProviderModelEntry]: +def get_model_entries() -> list[ProviderModelEntry]: return _MODEL_ENTRIES diff --git a/llama_stack/providers/remote/post_training/nvidia/post_training.py b/llama_stack/providers/remote/post_training/nvidia/post_training.py index e14fcf0cc..409818cb3 100644 --- a/llama_stack/providers/remote/post_training/nvidia/post_training.py +++ b/llama_stack/providers/remote/post_training/nvidia/post_training.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import warnings from datetime import datetime -from typing import Any, Dict, List, Literal, Optional +from typing import Any, Literal import aiohttp from pydantic import BaseModel, ConfigDict @@ -27,11 +27,12 @@ from .models import _MODEL_ENTRIES # Map API status to JobStatus enum STATUS_MAPPING = { - "running": "in_progress", - "completed": "completed", - "failed": "failed", - "cancelled": "cancelled", - "pending": "scheduled", + "running": JobStatus.in_progress.value, + "completed": JobStatus.completed.value, + "failed": JobStatus.failed.value, + "cancelled": JobStatus.cancelled.value, + "pending": JobStatus.scheduled.value, + "unknown": JobStatus.scheduled.value, } @@ -49,7 +50,7 @@ class NvidiaPostTrainingJob(PostTrainingJob): class ListNvidiaPostTrainingJobs(BaseModel): - data: List[NvidiaPostTrainingJob] + data: list[NvidiaPostTrainingJob] class NvidiaPostTrainingJobStatusResponse(PostTrainingJobStatusResponse): @@ -66,22 +67,27 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): self.timeout = aiohttp.ClientTimeout(total=config.timeout) # TODO: filter by available models based on /config endpoint ModelRegistryHelper.__init__(self, model_entries=_MODEL_ENTRIES) - self.session = aiohttp.ClientSession(headers=self.headers, timeout=self.timeout) - self.customizer_url = config.customizer_url + self.session = None + self.customizer_url = config.customizer_url if not self.customizer_url: warnings.warn("Customizer URL is not set, using default value: http://nemo.test", stacklevel=2) self.customizer_url = "http://nemo.test" + async def _get_session(self) -> aiohttp.ClientSession: + if self.session is None or self.session.closed: + self.session = aiohttp.ClientSession(headers=self.headers, timeout=self.timeout) + return self.session + async def _make_request( self, method: str, path: str, - headers: Optional[Dict[str, Any]] = None, - params: Optional[Dict[str, Any]] = None, - json: Optional[Dict[str, Any]] = None, + headers: dict[str, Any] | None = None, + params: dict[str, Any] | None = None, + json: dict[str, Any] | None = None, **kwargs, - ) -> Dict[str, Any]: + ) -> dict[str, Any]: """Helper method to make HTTP requests to the Customizer API.""" url = f"{self.customizer_url}{path}" request_headers = self.headers.copy() @@ -93,8 +99,9 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): if json and "Content-Type" not in request_headers: request_headers["Content-Type"] = "application/json" + session = await self._get_session() for _ in range(self.config.max_retries): - async with self.session.request(method, url, params=params, json=json, **kwargs) as response: + async with session.request(method, url, params=params, json=json, **kwargs) as response: if response.status >= 400: error_data = await response.json() raise Exception(f"API request failed: {error_data}") @@ -102,9 +109,9 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): async def get_training_jobs( self, - page: Optional[int] = 1, - page_size: Optional[int] = 10, - sort: Optional[Literal["created_at", "-created_at"]] = "created_at", + page: int | None = 1, + page_size: int | None = 10, + sort: Literal["created_at", "-created_at"] | None = "created_at", ) -> ListNvidiaPostTrainingJobs: """Get all customization jobs. Updated the base class return type from ListPostTrainingJobsResponse to ListNvidiaPostTrainingJobs. @@ -121,8 +128,8 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): jobs = [] for job in response.get("data", []): job_id = job.pop("id") - job_status = job.pop("status", "unknown").lower() - mapped_status = STATUS_MAPPING.get(job_status, "unknown") + job_status = job.pop("status", "scheduled").lower() + mapped_status = STATUS_MAPPING.get(job_status, "scheduled") # Convert string timestamps to datetime objects created_at = ( @@ -176,7 +183,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): ) api_status = response.pop("status").lower() - mapped_status = STATUS_MAPPING.get(api_status, "unknown") + mapped_status = STATUS_MAPPING.get(api_status, "scheduled") return NvidiaPostTrainingJobStatusResponse( status=JobStatus(mapped_status), @@ -200,12 +207,12 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): async def supervised_fine_tune( self, job_uuid: str, - training_config: Dict[str, Any], - hyperparam_search_config: Dict[str, Any], - logger_config: Dict[str, Any], + training_config: dict[str, Any], + hyperparam_search_config: dict[str, Any], + logger_config: dict[str, Any], model: str, - checkpoint_dir: Optional[str], - algorithm_config: Optional[AlgorithmConfig] = None, + checkpoint_dir: str | None, + algorithm_config: AlgorithmConfig | None = None, ) -> NvidiaPostTrainingJob: """ Fine-tunes a model on a dataset. @@ -238,6 +245,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): Supported models: - meta/llama-3.1-8b-instruct + - meta/llama-3.2-1b-instruct Supported algorithm configs: - LoRA, SFT @@ -283,10 +291,6 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): - LoRA config: ## NeMo customizer specific LoRA parameters - - adapter_dim: int - Adapter dimension - Default: 8 (supports powers of 2) - - adapter_dropout: float - Adapter dropout - Default: None (0.0-1.0) - alpha: int - Scaling factor for the LoRA update Default: 16 Note: @@ -296,7 +300,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): User is informed about unsupported parameters via warnings. """ # Map model to nvidia model name - # ToDo: only supports llama-3.1-8b-instruct now, need to update this to support other models + # See `_MODEL_ENTRIES` for supported models nvidia_model = self.get_provider_model_id(model) # Check for unsupported method parameters @@ -329,7 +333,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): }, "data_config": {"dataset_id", "batch_size"}, "optimizer_config": {"lr", "weight_decay"}, - "lora_config": {"type", "adapter_dim", "adapter_dropout", "alpha"}, + "lora_config": {"type", "alpha"}, } # Validate all parameters at once @@ -388,16 +392,10 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): # Handle LoRA-specific configuration if algorithm_config: - if isinstance(algorithm_config, dict) and algorithm_config.get("type") == "LoRA": + if algorithm_config.type == "LoRA": warn_unsupported_params(algorithm_config, supported_params["lora_config"], "LoRA config") job_config["hyperparameters"]["lora"] = { - k: v - for k, v in { - "adapter_dim": algorithm_config.get("adapter_dim"), - "alpha": algorithm_config.get("alpha"), - "adapter_dropout": algorithm_config.get("adapter_dropout"), - }.items() - if v is not None + k: v for k, v in {"alpha": algorithm_config.alpha}.items() if v is not None } else: raise NotImplementedError(f"Unsupported algorithm config: {algorithm_config}") @@ -425,8 +423,8 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): finetuned_model: str, algorithm_config: DPOAlignmentConfig, training_config: TrainingConfig, - hyperparam_search_config: Dict[str, Any], - logger_config: Dict[str, Any], + hyperparam_search_config: dict[str, Any], + logger_config: dict[str, Any], ) -> PostTrainingJob: """Optimize a model based on preference data.""" raise NotImplementedError("Preference optimization is not implemented yet") diff --git a/llama_stack/providers/remote/post_training/nvidia/utils.py b/llama_stack/providers/remote/post_training/nvidia/utils.py index ac47966af..d6e1016b2 100644 --- a/llama_stack/providers/remote/post_training/nvidia/utils.py +++ b/llama_stack/providers/remote/post_training/nvidia/utils.py @@ -6,7 +6,7 @@ import logging import warnings -from typing import Any, Dict, Set, Tuple +from typing import Any from pydantic import BaseModel @@ -18,7 +18,7 @@ from .config import NvidiaPostTrainingConfig logger = logging.getLogger(__name__) -def warn_unsupported_params(config_dict: Any, supported_keys: Set[str], config_name: str) -> None: +def warn_unsupported_params(config_dict: Any, supported_keys: set[str], config_name: str) -> None: keys = set(config_dict.__annotations__.keys()) if isinstance(config_dict, BaseModel) else config_dict.keys() unsupported_params = [k for k in keys if k not in supported_keys] if unsupported_params: @@ -28,7 +28,7 @@ def warn_unsupported_params(config_dict: Any, supported_keys: Set[str], config_n def validate_training_params( - training_config: Dict[str, Any], supported_keys: Set[str], config_name: str = "TrainingConfig" + training_config: dict[str, Any], supported_keys: set[str], config_name: str = "TrainingConfig" ) -> None: """ Validates training parameters against supported keys. @@ -57,7 +57,7 @@ def validate_training_params( # ToDo: implement post health checks for customizer are enabled -async def _get_health(url: str) -> Tuple[bool, bool]: ... +async def _get_health(url: str) -> tuple[bool, bool]: ... async def check_health(config: NvidiaPostTrainingConfig) -> None: ... diff --git a/llama_stack/providers/remote/safety/bedrock/bedrock.py b/llama_stack/providers/remote/safety/bedrock/bedrock.py index 2f960eead..c43b51073 100644 --- a/llama_stack/providers/remote/safety/bedrock/bedrock.py +++ b/llama_stack/providers/remote/safety/bedrock/bedrock.py @@ -6,7 +6,7 @@ import json import logging -from typing import Any, Dict, List +from typing import Any from llama_stack.apis.inference import Message from llama_stack.apis.safety import ( @@ -53,7 +53,7 @@ class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate): ) async def run_shield( - self, shield_id: str, messages: List[Message], params: Dict[str, Any] = None + self, shield_id: str, messages: list[Message], params: dict[str, Any] = None ) -> RunShieldResponse: shield = await self.shield_store.get_shield(shield_id) if not shield: diff --git a/llama_stack/providers/remote/safety/nvidia/README.md b/llama_stack/providers/remote/safety/nvidia/README.md new file mode 100644 index 000000000..434db32fb --- /dev/null +++ b/llama_stack/providers/remote/safety/nvidia/README.md @@ -0,0 +1,77 @@ +# NVIDIA Safety Provider for LlamaStack + +This provider enables safety checks and guardrails for LLM interactions using NVIDIA's NeMo Guardrails service. + +## Features + +- Run safety checks for messages + +## Getting Started + +### Prerequisites + +- LlamaStack with NVIDIA configuration +- Access to NVIDIA NeMo Guardrails service +- NIM for model to use for safety check is deployed + +### Setup + +Build the NVIDIA environment: + +```bash +llama stack build --template nvidia --image-type conda +``` + +### Basic Usage using the LlamaStack Python Client + +#### Initialize the client + +```python +import os + +os.environ["NVIDIA_API_KEY"] = "your-api-key" +os.environ["NVIDIA_GUARDRAILS_URL"] = "http://guardrails.test" + +from llama_stack.distribution.library_client import LlamaStackAsLibraryClient + +client = LlamaStackAsLibraryClient("nvidia") +client.initialize() +``` + +#### Create a safety shield + +```python +from llama_stack.apis.safety import Shield +from llama_stack.apis.inference import Message + +# Create a safety shield +shield = Shield( + shield_id="your-shield-id", + provider_resource_id="safety-model-id", # The model to use for safety checks + description="Safety checks for content moderation", +) + +# Register the shield +await client.safety.register_shield(shield) +``` + +#### Run safety checks + +```python +# Messages to check +messages = [Message(role="user", content="Your message to check")] + +# Run safety check +response = await client.safety.run_shield( + shield_id="your-shield-id", + messages=messages, +) + +# Check for violations +if response.violation: + print(f"Safety violation detected: {response.violation.user_message}") + print(f"Violation level: {response.violation.violation_level}") + print(f"Metadata: {response.violation.metadata}") +else: + print("No safety violations detected") +``` diff --git a/llama_stack/providers/remote/safety/nvidia/config.py b/llama_stack/providers/remote/safety/nvidia/config.py index 3df80ed4f..4ca703a4d 100644 --- a/llama_stack/providers/remote/safety/nvidia/config.py +++ b/llama_stack/providers/remote/safety/nvidia/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. import os -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field @@ -27,10 +27,10 @@ class NVIDIASafetyConfig(BaseModel): default_factory=lambda: os.getenv("GUARDRAILS_SERVICE_URL", "http://0.0.0.0:7331"), description="The url for accessing the guardrails service", ) - config_id: Optional[str] = Field(default="self-check", description="Config ID to use from the config store") + config_id: str | None = Field(default="self-check", description="Config ID to use from the config store") @classmethod - def sample_run_config(cls, **kwargs) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs) -> dict[str, Any]: return { "guardrails_service_url": "${env.GUARDRAILS_SERVICE_URL:http://localhost:7331}", "config_id": "self-check", diff --git a/llama_stack/providers/remote/safety/nvidia/nvidia.py b/llama_stack/providers/remote/safety/nvidia/nvidia.py index 1ff4a6ad9..411badb1c 100644 --- a/llama_stack/providers/remote/safety/nvidia/nvidia.py +++ b/llama_stack/providers/remote/safety/nvidia/nvidia.py @@ -5,15 +5,15 @@ # the root directory of this source tree. import logging -from typing import Any, List, Optional +from typing import Any import requests from llama_stack.apis.inference import Message from llama_stack.apis.safety import RunShieldResponse, Safety, SafetyViolation, ViolationLevel from llama_stack.apis.shields import Shield -from llama_stack.distribution.library_client import convert_pydantic_to_json_value from llama_stack.providers.datatypes import ShieldsProtocolPrivate +from llama_stack.providers.utils.inference.openai_compat import convert_message_to_openai_dict_new from .config import NVIDIASafetyConfig @@ -28,7 +28,6 @@ class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate): Args: config (NVIDIASafetyConfig): The configuration containing the guardrails service URL and config ID. """ - print(f"Initializing NVIDIASafetyAdapter({config.guardrails_service_url})...") self.config = config async def initialize(self) -> None: @@ -42,7 +41,7 @@ class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate): raise ValueError("Shield model not provided.") async def run_shield( - self, shield_id: str, messages: List[Message], params: Optional[dict[str, Any]] = None + self, shield_id: str, messages: list[Message], params: dict[str, Any] | None = None ) -> RunShieldResponse: """ Run a safety shield check against the provided messages. @@ -113,7 +112,7 @@ class NeMoGuardrails: response.raise_for_status() return response.json() - async def run(self, messages: List[Message]) -> RunShieldResponse: + async def run(self, messages: list[Message]) -> RunShieldResponse: """ Queries the /v1/guardrails/checks endpoint of the NeMo guardrails deployed API. @@ -127,9 +126,10 @@ class NeMoGuardrails: Raises: requests.HTTPError: If the POST request fails. """ + request_messages = [await convert_message_to_openai_dict_new(message) for message in messages] request_data = { "model": self.model, - "messages": convert_pydantic_to_json_value(messages), + "messages": request_messages, "temperature": self.temperature, "top_p": 1, "frequency_penalty": 0, diff --git a/llama_stack/providers/remote/tool_runtime/bing_search/bing_search.py b/llama_stack/providers/remote/tool_runtime/bing_search/bing_search.py index b34c9fd9d..18bec463f 100644 --- a/llama_stack/providers/remote/tool_runtime/bing_search/bing_search.py +++ b/llama_stack/providers/remote/tool_runtime/bing_search/bing_search.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import json -from typing import Any, Dict, Optional +from typing import Any import httpx @@ -50,7 +50,7 @@ class BingSearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestP return provider_data.bing_search_api_key async def list_runtime_tools( - self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None + self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None ) -> ListToolDefsResponse: return ListToolDefsResponse( data=[ @@ -68,7 +68,7 @@ class BingSearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestP ] ) - async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> ToolInvocationResult: + async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult: api_key = self._get_api_key() headers = { "Ocp-Apim-Subscription-Key": api_key, diff --git a/llama_stack/providers/remote/tool_runtime/bing_search/config.py b/llama_stack/providers/remote/tool_runtime/bing_search/config.py index 4f089439f..30269dbc1 100644 --- a/llama_stack/providers/remote/tool_runtime/bing_search/config.py +++ b/llama_stack/providers/remote/tool_runtime/bing_search/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel @@ -12,11 +12,11 @@ from pydantic import BaseModel class BingSearchToolConfig(BaseModel): """Configuration for Bing Search Tool Runtime""" - api_key: Optional[str] = None + api_key: str | None = None top_k: int = 3 @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return { "api_key": "${env.BING_API_KEY:}", } diff --git a/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py b/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py index 41f3ce823..355cb98b6 100644 --- a/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py +++ b/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any import httpx @@ -49,7 +49,7 @@ class BraveSearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequest return provider_data.brave_search_api_key async def list_runtime_tools( - self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None + self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None ) -> ListToolDefsResponse: return ListToolDefsResponse( data=[ @@ -68,7 +68,7 @@ class BraveSearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequest ] ) - async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> ToolInvocationResult: + async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult: api_key = self._get_api_key() url = "https://api.search.brave.com/res/v1/web/search" headers = { diff --git a/llama_stack/providers/remote/tool_runtime/brave_search/config.py b/llama_stack/providers/remote/tool_runtime/brave_search/config.py index ab6053609..37ba21304 100644 --- a/llama_stack/providers/remote/tool_runtime/brave_search/config.py +++ b/llama_stack/providers/remote/tool_runtime/brave_search/config.py @@ -4,13 +4,13 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field class BraveSearchToolConfig(BaseModel): - api_key: Optional[str] = Field( + api_key: str | None = Field( default=None, description="The Brave Search API Key", ) @@ -20,7 +20,7 @@ class BraveSearchToolConfig(BaseModel): ) @classmethod - def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]: return { "api_key": "${env.BRAVE_SEARCH_API_KEY:}", "max_results": 3, diff --git a/llama_stack/providers/remote/tool_runtime/model_context_protocol/config.py b/llama_stack/providers/remote/tool_runtime/model_context_protocol/config.py index 30ac407bc..d509074fc 100644 --- a/llama_stack/providers/remote/tool_runtime/model_context_protocol/config.py +++ b/llama_stack/providers/remote/tool_runtime/model_context_protocol/config.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel class ModelContextProtocolConfig(BaseModel): @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return {} diff --git a/llama_stack/providers/remote/tool_runtime/model_context_protocol/model_context_protocol.py b/llama_stack/providers/remote/tool_runtime/model_context_protocol/model_context_protocol.py index 676917225..142730e89 100644 --- a/llama_stack/providers/remote/tool_runtime/model_context_protocol/model_context_protocol.py +++ b/llama_stack/providers/remote/tool_runtime/model_context_protocol/model_context_protocol.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from urllib.parse import urlparse from mcp import ClientSession @@ -31,7 +31,7 @@ class ModelContextProtocolToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime): pass async def list_runtime_tools( - self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None + self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None ) -> ListToolDefsResponse: if mcp_endpoint is None: raise ValueError("mcp_endpoint is required") @@ -63,7 +63,7 @@ class ModelContextProtocolToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime): ) return ListToolDefsResponse(data=tools) - async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> ToolInvocationResult: + async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult: tool = await self.tool_store.get_tool(tool_name) if tool.metadata is None or tool.metadata.get("endpoint") is None: raise ValueError(f"Tool {tool_name} does not have metadata") diff --git a/llama_stack/providers/remote/tool_runtime/tavily_search/config.py b/llama_stack/providers/remote/tool_runtime/tavily_search/config.py index 945430bb1..c9b18d30d 100644 --- a/llama_stack/providers/remote/tool_runtime/tavily_search/config.py +++ b/llama_stack/providers/remote/tool_runtime/tavily_search/config.py @@ -4,13 +4,13 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel, Field class TavilySearchToolConfig(BaseModel): - api_key: Optional[str] = Field( + api_key: str | None = Field( default=None, description="The Tavily Search API Key", ) @@ -20,7 +20,7 @@ class TavilySearchToolConfig(BaseModel): ) @classmethod - def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]: return { "api_key": "${env.TAVILY_SEARCH_API_KEY:}", "max_results": 3, diff --git a/llama_stack/providers/remote/tool_runtime/tavily_search/tavily_search.py b/llama_stack/providers/remote/tool_runtime/tavily_search/tavily_search.py index 719d6be14..9d6fcd951 100644 --- a/llama_stack/providers/remote/tool_runtime/tavily_search/tavily_search.py +++ b/llama_stack/providers/remote/tool_runtime/tavily_search/tavily_search.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import json -from typing import Any, Dict, Optional +from typing import Any import httpx @@ -49,7 +49,7 @@ class TavilySearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsReques return provider_data.tavily_search_api_key async def list_runtime_tools( - self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None + self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None ) -> ListToolDefsResponse: return ListToolDefsResponse( data=[ @@ -67,7 +67,7 @@ class TavilySearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsReques ] ) - async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> ToolInvocationResult: + async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult: api_key = self._get_api_key() async with httpx.AsyncClient() as client: response = await client.post( diff --git a/llama_stack/providers/remote/tool_runtime/wolfram_alpha/config.py b/llama_stack/providers/remote/tool_runtime/wolfram_alpha/config.py index 8ea49c7b5..aefc86bd6 100644 --- a/llama_stack/providers/remote/tool_runtime/wolfram_alpha/config.py +++ b/llama_stack/providers/remote/tool_runtime/wolfram_alpha/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel @@ -12,10 +12,10 @@ from pydantic import BaseModel class WolframAlphaToolConfig(BaseModel): """Configuration for WolframAlpha Tool Runtime""" - api_key: Optional[str] = None + api_key: str | None = None @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return { "api_key": "${env.WOLFRAM_ALPHA_API_KEY:}", } diff --git a/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py b/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py index b3e0e120c..a3724e4b4 100644 --- a/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py +++ b/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import json -from typing import Any, Dict, Optional +from typing import Any import httpx @@ -50,7 +50,7 @@ class WolframAlphaToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsReques return provider_data.wolfram_alpha_api_key async def list_runtime_tools( - self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None + self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None ) -> ListToolDefsResponse: return ListToolDefsResponse( data=[ @@ -68,7 +68,7 @@ class WolframAlphaToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsReques ] ) - async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> ToolInvocationResult: + async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult: api_key = self._get_api_key() params = { "input": kwargs["query"], diff --git a/llama_stack/providers/remote/vector_io/chroma/__init__.py b/llama_stack/providers/remote/vector_io/chroma/__init__.py index 8646b04d6..ebbc62b1c 100644 --- a/llama_stack/providers/remote/vector_io/chroma/__init__.py +++ b/llama_stack/providers/remote/vector_io/chroma/__init__.py @@ -4,14 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Dict - from llama_stack.providers.datatypes import Api, ProviderSpec from .config import ChromaVectorIOConfig -async def get_adapter_impl(config: ChromaVectorIOConfig, deps: Dict[Api, ProviderSpec]): +async def get_adapter_impl(config: ChromaVectorIOConfig, deps: dict[Api, ProviderSpec]): from .chroma import ChromaVectorIOAdapter impl = ChromaVectorIOAdapter(config, deps[Api.inference]) diff --git a/llama_stack/providers/remote/vector_io/chroma/chroma.py b/llama_stack/providers/remote/vector_io/chroma/chroma.py index 3bf3a7740..5381a48ef 100644 --- a/llama_stack/providers/remote/vector_io/chroma/chroma.py +++ b/llama_stack/providers/remote/vector_io/chroma/chroma.py @@ -6,7 +6,7 @@ import asyncio import json import logging -from typing import Any, Dict, List, Optional, Union +from typing import Any from urllib.parse import urlparse import chromadb @@ -27,7 +27,7 @@ from .config import ChromaVectorIOConfig as RemoteChromaVectorIOConfig log = logging.getLogger(__name__) -ChromaClientType = Union[chromadb.AsyncHttpClient, chromadb.PersistentClient] +ChromaClientType = chromadb.AsyncHttpClient | chromadb.PersistentClient # this is a helper to allow us to use async and non-async chroma clients interchangeably @@ -42,7 +42,7 @@ class ChromaIndex(EmbeddingIndex): self.client = client self.collection = collection - async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray): + async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): assert len(chunks) == len(embeddings), ( f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}" ) @@ -89,7 +89,7 @@ class ChromaIndex(EmbeddingIndex): class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): def __init__( self, - config: Union[RemoteChromaVectorIOConfig, InlineChromaVectorIOConfig], + config: RemoteChromaVectorIOConfig | InlineChromaVectorIOConfig, inference_api: Api.inference, ) -> None: log.info(f"Initializing ChromaVectorIOAdapter with url: {config}") @@ -137,8 +137,8 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): async def insert_chunks( self, vector_db_id: str, - chunks: List[Chunk], - ttl_seconds: Optional[int] = None, + chunks: list[Chunk], + ttl_seconds: int | None = None, ) -> None: index = await self._get_and_cache_vector_db_index(vector_db_id) @@ -148,7 +148,7 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): self, vector_db_id: str, query: InterleavedContent, - params: Optional[Dict[str, Any]] = None, + params: dict[str, Any] | None = None, ) -> QueryChunksResponse: index = await self._get_and_cache_vector_db_index(vector_db_id) diff --git a/llama_stack/providers/remote/vector_io/chroma/config.py b/llama_stack/providers/remote/vector_io/chroma/config.py index 3e2463252..4e893fab4 100644 --- a/llama_stack/providers/remote/vector_io/chroma/config.py +++ b/llama_stack/providers/remote/vector_io/chroma/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -13,5 +13,5 @@ class ChromaVectorIOConfig(BaseModel): url: str @classmethod - def sample_run_config(cls, url: str = "${env.CHROMADB_URL}", **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, url: str = "${env.CHROMADB_URL}", **kwargs: Any) -> dict[str, Any]: return {"url": url} diff --git a/llama_stack/providers/remote/vector_io/milvus/__init__.py b/llama_stack/providers/remote/vector_io/milvus/__init__.py index 84cb1d748..92dbfda2e 100644 --- a/llama_stack/providers/remote/vector_io/milvus/__init__.py +++ b/llama_stack/providers/remote/vector_io/milvus/__init__.py @@ -4,14 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Dict - from llama_stack.providers.datatypes import Api, ProviderSpec from .config import MilvusVectorIOConfig -async def get_adapter_impl(config: MilvusVectorIOConfig, deps: Dict[Api, ProviderSpec]): +async def get_adapter_impl(config: MilvusVectorIOConfig, deps: dict[Api, ProviderSpec]): from .milvus import MilvusVectorIOAdapter assert isinstance(config, MilvusVectorIOConfig), f"Unexpected config type: {type(config)}" diff --git a/llama_stack/providers/remote/vector_io/milvus/config.py b/llama_stack/providers/remote/vector_io/milvus/config.py index 17da6b23d..3d25e9c49 100644 --- a/llama_stack/providers/remote/vector_io/milvus/config.py +++ b/llama_stack/providers/remote/vector_io/milvus/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel @@ -14,9 +14,9 @@ from llama_stack.schema_utils import json_schema_type @json_schema_type class MilvusVectorIOConfig(BaseModel): uri: str - token: Optional[str] = None + token: str | None = None consistency_level: str = "Strong" @classmethod - def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return {"uri": "${env.MILVUS_ENDPOINT}", "token": "${env.MILVUS_TOKEN}"} diff --git a/llama_stack/providers/remote/vector_io/milvus/milvus.py b/llama_stack/providers/remote/vector_io/milvus/milvus.py index 1949d293d..c98417b56 100644 --- a/llama_stack/providers/remote/vector_io/milvus/milvus.py +++ b/llama_stack/providers/remote/vector_io/milvus/milvus.py @@ -9,7 +9,7 @@ import hashlib import logging import os import uuid -from typing import Any, Dict, List, Optional, Union +from typing import Any from numpy.typing import NDArray from pymilvus import MilvusClient @@ -39,7 +39,7 @@ class MilvusIndex(EmbeddingIndex): if await asyncio.to_thread(self.client.has_collection, self.collection_name): await asyncio.to_thread(self.client.drop_collection, collection_name=self.collection_name) - async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray): + async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): assert len(chunks) == len(embeddings), ( f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}" ) @@ -89,7 +89,7 @@ class MilvusIndex(EmbeddingIndex): class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): def __init__( - self, config: Union[RemoteMilvusVectorIOConfig, InlineMilvusVectorIOConfig], inference_api: Api.inference + self, config: RemoteMilvusVectorIOConfig | InlineMilvusVectorIOConfig, inference_api: Api.inference ) -> None: self.config = config self.cache = {} @@ -124,7 +124,7 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): self.cache[vector_db.identifier] = index - async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> Optional[VectorDBWithIndex]: + async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> VectorDBWithIndex | None: if vector_db_id in self.cache: return self.cache[vector_db_id] @@ -148,8 +148,8 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): async def insert_chunks( self, vector_db_id: str, - chunks: List[Chunk], - ttl_seconds: Optional[int] = None, + chunks: list[Chunk], + ttl_seconds: int | None = None, ) -> None: index = await self._get_and_cache_vector_db_index(vector_db_id) if not index: @@ -161,7 +161,7 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): self, vector_db_id: str, query: InterleavedContent, - params: Optional[Dict[str, Any]] = None, + params: dict[str, Any] | None = None, ) -> QueryChunksResponse: index = await self._get_and_cache_vector_db_index(vector_db_id) if not index: @@ -172,7 +172,7 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): def generate_chunk_id(document_id: str, chunk_text: str) -> str: """Generate a unique chunk ID using a hash of document ID and chunk text.""" - hash_input = f"{document_id}:{chunk_text}".encode("utf-8") + hash_input = f"{document_id}:{chunk_text}".encode() return str(uuid.UUID(hashlib.md5(hash_input).hexdigest())) diff --git a/llama_stack/providers/remote/vector_io/pgvector/__init__.py b/llama_stack/providers/remote/vector_io/pgvector/__init__.py index 089d890b7..9f528db74 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/__init__.py +++ b/llama_stack/providers/remote/vector_io/pgvector/__init__.py @@ -4,14 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Dict - from llama_stack.providers.datatypes import Api, ProviderSpec from .config import PGVectorVectorIOConfig -async def get_adapter_impl(config: PGVectorVectorIOConfig, deps: Dict[Api, ProviderSpec]): +async def get_adapter_impl(config: PGVectorVectorIOConfig, deps: dict[Api, ProviderSpec]): from .pgvector import PGVectorVectorIOAdapter impl = PGVectorVectorIOAdapter(config, deps[Api.inference]) diff --git a/llama_stack/providers/remote/vector_io/pgvector/config.py b/llama_stack/providers/remote/vector_io/pgvector/config.py index e9eb0f12d..04b92a2e4 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/config.py +++ b/llama_stack/providers/remote/vector_io/pgvector/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel, Field @@ -28,5 +28,5 @@ class PGVectorVectorIOConfig(BaseModel): user: str = "${env.PGVECTOR_USER}", password: str = "${env.PGVECTOR_PASSWORD}", **kwargs: Any, - ) -> Dict[str, Any]: + ) -> dict[str, Any]: return {"host": host, "port": port, "db": db, "user": user, "password": password} diff --git a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py index 7c683e126..94546c6cf 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py +++ b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import logging -from typing import Any, Dict, List, Optional, Tuple +from typing import Any import psycopg2 from numpy.typing import NDArray @@ -33,7 +33,7 @@ def check_extension_version(cur): return result[0] if result else None -def upsert_models(conn, keys_models: List[Tuple[str, BaseModel]]): +def upsert_models(conn, keys_models: list[tuple[str, BaseModel]]): with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: query = sql.SQL( """ @@ -74,7 +74,7 @@ class PGVectorIndex(EmbeddingIndex): """ ) - async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray): + async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): assert len(chunks) == len(embeddings), ( f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}" ) @@ -180,8 +180,8 @@ class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): async def insert_chunks( self, vector_db_id: str, - chunks: List[Chunk], - ttl_seconds: Optional[int] = None, + chunks: list[Chunk], + ttl_seconds: int | None = None, ) -> None: index = await self._get_and_cache_vector_db_index(vector_db_id) await index.insert_chunks(chunks) @@ -190,7 +190,7 @@ class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): self, vector_db_id: str, query: InterleavedContent, - params: Optional[Dict[str, Any]] = None, + params: dict[str, Any] | None = None, ) -> QueryChunksResponse: index = await self._get_and_cache_vector_db_index(vector_db_id) return await index.query_chunks(query, params) diff --git a/llama_stack/providers/remote/vector_io/qdrant/__init__.py b/llama_stack/providers/remote/vector_io/qdrant/__init__.py index f5bb7f84c..029de285f 100644 --- a/llama_stack/providers/remote/vector_io/qdrant/__init__.py +++ b/llama_stack/providers/remote/vector_io/qdrant/__init__.py @@ -4,14 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Dict - from llama_stack.providers.datatypes import Api, ProviderSpec from .config import QdrantVectorIOConfig -async def get_adapter_impl(config: QdrantVectorIOConfig, deps: Dict[Api, ProviderSpec]): +async def get_adapter_impl(config: QdrantVectorIOConfig, deps: dict[Api, ProviderSpec]): from .qdrant import QdrantVectorIOAdapter impl = QdrantVectorIOAdapter(config, deps[Api.inference]) diff --git a/llama_stack/providers/remote/vector_io/qdrant/config.py b/llama_stack/providers/remote/vector_io/qdrant/config.py index 6d7eebe23..314d3f5f1 100644 --- a/llama_stack/providers/remote/vector_io/qdrant/config.py +++ b/llama_stack/providers/remote/vector_io/qdrant/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, Optional +from typing import Any from pydantic import BaseModel @@ -13,19 +13,19 @@ from llama_stack.schema_utils import json_schema_type @json_schema_type class QdrantVectorIOConfig(BaseModel): - location: Optional[str] = None - url: Optional[str] = None - port: Optional[int] = 6333 + location: str | None = None + url: str | None = None + port: int | None = 6333 grpc_port: int = 6334 prefer_grpc: bool = False - https: Optional[bool] = None - api_key: Optional[str] = None - prefix: Optional[str] = None - timeout: Optional[int] = None - host: Optional[str] = None + https: bool | None = None + api_key: str | None = None + prefix: str | None = None + timeout: int | None = None + host: str | None = None @classmethod - def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs: Any) -> dict[str, Any]: return { "api_key": "${env.QDRANT_API_KEY}", } diff --git a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py index 9e7788dc0..514a6c70d 100644 --- a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py +++ b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py @@ -6,7 +6,7 @@ import logging import uuid -from typing import Any, Dict, List, Optional, Union +from typing import Any from numpy.typing import NDArray from qdrant_client import AsyncQdrantClient, models @@ -44,7 +44,7 @@ class QdrantIndex(EmbeddingIndex): self.client = client self.collection_name = collection_name - async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray): + async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): assert len(chunks) == len(embeddings), ( f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}" ) @@ -101,7 +101,7 @@ class QdrantIndex(EmbeddingIndex): class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): def __init__( - self, config: Union[RemoteQdrantVectorIOConfig, InlineQdrantVectorIOConfig], inference_api: Api.inference + self, config: RemoteQdrantVectorIOConfig | InlineQdrantVectorIOConfig, inference_api: Api.inference ) -> None: self.config = config self.client: AsyncQdrantClient = None @@ -131,7 +131,7 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): await self.cache[vector_db_id].index.delete() del self.cache[vector_db_id] - async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> Optional[VectorDBWithIndex]: + async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> VectorDBWithIndex | None: if vector_db_id in self.cache: return self.cache[vector_db_id] @@ -150,8 +150,8 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): async def insert_chunks( self, vector_db_id: str, - chunks: List[Chunk], - ttl_seconds: Optional[int] = None, + chunks: list[Chunk], + ttl_seconds: int | None = None, ) -> None: index = await self._get_and_cache_vector_db_index(vector_db_id) if not index: @@ -163,7 +163,7 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): self, vector_db_id: str, query: InterleavedContent, - params: Optional[Dict[str, Any]] = None, + params: dict[str, Any] | None = None, ) -> QueryChunksResponse: index = await self._get_and_cache_vector_db_index(vector_db_id) if not index: diff --git a/llama_stack/providers/remote/vector_io/weaviate/__init__.py b/llama_stack/providers/remote/vector_io/weaviate/__init__.py index c93c628d8..22e116c22 100644 --- a/llama_stack/providers/remote/vector_io/weaviate/__init__.py +++ b/llama_stack/providers/remote/vector_io/weaviate/__init__.py @@ -4,14 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Dict - from llama_stack.providers.datatypes import Api, ProviderSpec -from .config import WeaviateRequestProviderData, WeaviateVectorIOConfig # noqa: F401 +from .config import WeaviateVectorIOConfig -async def get_adapter_impl(config: WeaviateVectorIOConfig, deps: Dict[Api, ProviderSpec]): +async def get_adapter_impl(config: WeaviateVectorIOConfig, deps: dict[Api, ProviderSpec]): from .weaviate import WeaviateVectorIOAdapter impl = WeaviateVectorIOAdapter(config, deps[Api.inference]) diff --git a/llama_stack/providers/remote/vector_io/weaviate/config.py b/llama_stack/providers/remote/vector_io/weaviate/config.py index cc587f252..a8c6e3e2c 100644 --- a/llama_stack/providers/remote/vector_io/weaviate/config.py +++ b/llama_stack/providers/remote/vector_io/weaviate/config.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from pydantic import BaseModel @@ -16,5 +16,5 @@ class WeaviateRequestProviderData(BaseModel): class WeaviateVectorIOConfig(BaseModel): @classmethod - def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs: Any) -> dict[str, Any]: return {} diff --git a/llama_stack/providers/remote/vector_io/weaviate/weaviate.py b/llama_stack/providers/remote/vector_io/weaviate/weaviate.py index 52aa2f3a3..308d2eb3d 100644 --- a/llama_stack/providers/remote/vector_io/weaviate/weaviate.py +++ b/llama_stack/providers/remote/vector_io/weaviate/weaviate.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import json import logging -from typing import Any, Dict, List, Optional +from typing import Any import weaviate import weaviate.classes as wvc @@ -33,7 +33,7 @@ class WeaviateIndex(EmbeddingIndex): self.client = client self.collection_name = collection_name - async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray): + async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): assert len(chunks) == len(embeddings), ( f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}" ) @@ -80,7 +80,7 @@ class WeaviateIndex(EmbeddingIndex): return QueryChunksResponse(chunks=chunks, scores=scores) - async def delete(self, chunk_ids: List[str]) -> None: + async def delete(self, chunk_ids: list[str]) -> None: collection = self.client.collections.get(self.collection_name) collection.data.delete_many(where=Filter.by_property("id").contains_any(chunk_ids)) @@ -144,7 +144,7 @@ class WeaviateVectorIOAdapter( self.inference_api, ) - async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> Optional[VectorDBWithIndex]: + async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> VectorDBWithIndex | None: if vector_db_id in self.cache: return self.cache[vector_db_id] @@ -167,8 +167,8 @@ class WeaviateVectorIOAdapter( async def insert_chunks( self, vector_db_id: str, - chunks: List[Chunk], - ttl_seconds: Optional[int] = None, + chunks: list[Chunk], + ttl_seconds: int | None = None, ) -> None: index = await self._get_and_cache_vector_db_index(vector_db_id) if not index: @@ -180,7 +180,7 @@ class WeaviateVectorIOAdapter( self, vector_db_id: str, query: InterleavedContent, - params: Optional[Dict[str, Any]] = None, + params: dict[str, Any] | None = None, ) -> QueryChunksResponse: index = await self._get_and_cache_vector_db_index(vector_db_id) if not index: diff --git a/llama_stack/providers/tests/conftest.py b/llama_stack/providers/tests/conftest.py index d3e715b7e..cd86af0d6 100644 --- a/llama_stack/providers/tests/conftest.py +++ b/llama_stack/providers/tests/conftest.py @@ -7,7 +7,7 @@ import os from collections import defaultdict from pathlib import Path -from typing import Any, Dict, List, Optional +from typing import Any import pytest import yaml @@ -23,36 +23,36 @@ from .report import Report class ProviderFixture(BaseModel): - providers: List[Provider] - provider_data: Optional[Dict[str, Any]] = None + providers: list[Provider] + provider_data: dict[str, Any] | None = None class TestScenario(BaseModel): # provider fixtures can be either a mark or a dictionary of api -> providers - provider_fixtures: Dict[str, str] = Field(default_factory=dict) - fixture_combo_id: Optional[str] = None + provider_fixtures: dict[str, str] = Field(default_factory=dict) + fixture_combo_id: str | None = None class APITestConfig(BaseModel): - scenarios: List[TestScenario] = Field(default_factory=list) - inference_models: List[str] = Field(default_factory=list) + scenarios: list[TestScenario] = Field(default_factory=list) + inference_models: list[str] = Field(default_factory=list) # test name format should be :: - tests: List[str] = Field(default_factory=list) + tests: list[str] = Field(default_factory=list) class MemoryApiTestConfig(APITestConfig): - embedding_model: Optional[str] = Field(default_factory=None) + embedding_model: str | None = Field(default_factory=None) class AgentsApiTestConfig(APITestConfig): - safety_shield: Optional[str] = Field(default_factory=None) + safety_shield: str | None = Field(default_factory=None) class TestConfig(BaseModel): - inference: Optional[APITestConfig] = None - agents: Optional[AgentsApiTestConfig] = None - memory: Optional[MemoryApiTestConfig] = None + inference: APITestConfig | None = None + agents: AgentsApiTestConfig | None = None + memory: MemoryApiTestConfig | None = None def get_test_config_from_config_file(metafunc_config): @@ -65,7 +65,7 @@ def get_test_config_from_config_file(metafunc_config): raise ValueError( f"Test config {config_file} was specified but not found. Please make sure it exists in the llama_stack/providers/tests directory." ) - with open(config_file_path, "r") as config_file: + with open(config_file_path) as config_file: config = yaml.safe_load(config_file) return TestConfig(**config) @@ -188,18 +188,18 @@ def pytest_addoption(parser): ) -def make_provider_id(providers: Dict[str, str]) -> str: +def make_provider_id(providers: dict[str, str]) -> str: return ":".join(f"{api}={provider}" for api, provider in sorted(providers.items())) -def get_provider_marks(providers: Dict[str, str]) -> List[Any]: +def get_provider_marks(providers: dict[str, str]) -> list[Any]: marks = [] for provider in providers.values(): marks.append(getattr(pytest.mark, provider)) return marks -def get_provider_fixture_overrides(config, available_fixtures: Dict[str, List[str]]) -> Optional[List[pytest.param]]: +def get_provider_fixture_overrides(config, available_fixtures: dict[str, list[str]]) -> list[pytest.param] | None: provider_str = config.getoption("--providers") if not provider_str: return None @@ -214,7 +214,7 @@ def get_provider_fixture_overrides(config, available_fixtures: Dict[str, List[st ] -def parse_fixture_string(provider_str: str, available_fixtures: Dict[str, List[str]]) -> Dict[str, str]: +def parse_fixture_string(provider_str: str, available_fixtures: dict[str, list[str]]) -> dict[str, str]: """Parse provider string of format 'api1=provider1,api2=provider2'""" if not provider_str: return {} diff --git a/llama_stack/providers/utils/bedrock/config.py b/llama_stack/providers/utils/bedrock/config.py index 95019666b..b25617d76 100644 --- a/llama_stack/providers/utils/bedrock/config.py +++ b/llama_stack/providers/utils/bedrock/config.py @@ -3,54 +3,53 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Optional from pydantic import BaseModel, Field class BedrockBaseConfig(BaseModel): - aws_access_key_id: Optional[str] = Field( + aws_access_key_id: str | None = Field( default=None, description="The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID", ) - aws_secret_access_key: Optional[str] = Field( + aws_secret_access_key: str | None = Field( default=None, description="The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY", ) - aws_session_token: Optional[str] = Field( + aws_session_token: str | None = Field( default=None, description="The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN", ) - region_name: Optional[str] = Field( + region_name: str | None = Field( default=None, description="The default AWS Region to use, for example, us-west-1 or us-west-2." "Default use environment variable: AWS_DEFAULT_REGION", ) - profile_name: Optional[str] = Field( + profile_name: str | None = Field( default=None, description="The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE", ) - total_max_attempts: Optional[int] = Field( + total_max_attempts: int | None = Field( default=None, description="An integer representing the maximum number of attempts that will be made for a single request, " "including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS", ) - retry_mode: Optional[str] = Field( + retry_mode: str | None = Field( default=None, description="A string representing the type of retries Boto3 will perform." "Default use environment variable: AWS_RETRY_MODE", ) - connect_timeout: Optional[float] = Field( + connect_timeout: float | None = Field( default=60, description="The time in seconds till a timeout exception is thrown when attempting to make a connection. " "The default is 60 seconds.", ) - read_timeout: Optional[float] = Field( + read_timeout: float | None = Field( default=60, description="The time in seconds till a timeout exception is thrown when attempting to read from a connection." "The default is 60 seconds.", ) - session_ttl: Optional[int] = Field( + session_ttl: int | None = Field( default=3600, description="The time in seconds till a session expires. The default is 3600 seconds (1 hour).", ) diff --git a/llama_stack/providers/utils/common/data_schema_validator.py b/llama_stack/providers/utils/common/data_schema_validator.py index eb9d9dd60..28a243863 100644 --- a/llama_stack/providers/utils/common/data_schema_validator.py +++ b/llama_stack/providers/utils/common/data_schema_validator.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from enum import Enum -from typing import Any, Dict, List +from typing import Any from llama_stack.apis.common.type_system import ( ChatCompletionInputType, @@ -85,16 +85,16 @@ def get_valid_schemas(api_str: str): def validate_dataset_schema( - dataset_schema: Dict[str, Any], - expected_schemas: List[Dict[str, Any]], + dataset_schema: dict[str, Any], + expected_schemas: list[dict[str, Any]], ): if dataset_schema not in expected_schemas: raise ValueError(f"Dataset {dataset_schema} does not have a correct input schema in {expected_schemas}") def validate_row_schema( - input_row: Dict[str, Any], - expected_schemas: List[Dict[str, Any]], + input_row: dict[str, Any], + expected_schemas: list[dict[str, Any]], ): for schema in expected_schemas: if all(key in input_row for key in schema): diff --git a/llama_stack/providers/utils/datasetio/pagination.py b/llama_stack/providers/utils/datasetio/pagination.py index 1b693f8f5..033022491 100644 --- a/llama_stack/providers/utils/datasetio/pagination.py +++ b/llama_stack/providers/utils/datasetio/pagination.py @@ -4,13 +4,13 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List +from typing import Any from llama_stack.apis.common.responses import PaginatedResponse def paginate_records( - records: List[Dict[str, Any]], + records: list[dict[str, Any]], start_index: int | None = None, limit: int | None = None, ) -> PaginatedResponse: diff --git a/llama_stack/providers/utils/inference/__init__.py b/llama_stack/providers/utils/inference/__init__.py index e36be9404..66269d173 100644 --- a/llama_stack/providers/utils/inference/__init__.py +++ b/llama_stack/providers/utils/inference/__init__.py @@ -4,8 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List - from llama_stack.models.llama.sku_list import all_registered_models from llama_stack.models.llama.sku_types import * # noqa: F403 @@ -22,7 +20,7 @@ def is_supported_safety_model(model: Model) -> bool: ] -def supported_inference_models() -> List[Model]: +def supported_inference_models() -> list[Model]: return [ m for m in all_registered_models() diff --git a/llama_stack/providers/utils/inference/embedding_mixin.py b/llama_stack/providers/utils/inference/embedding_mixin.py index 8b14c7502..7c8144c62 100644 --- a/llama_stack/providers/utils/inference/embedding_mixin.py +++ b/llama_stack/providers/utils/inference/embedding_mixin.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import logging -from typing import TYPE_CHECKING, List, Optional +from typing import TYPE_CHECKING if TYPE_CHECKING: from sentence_transformers import SentenceTransformer @@ -31,10 +31,10 @@ class SentenceTransformerEmbeddingMixin: async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: model = await self.model_store.get_model(model_id) embedding_model = self._load_sentence_transformer_model(model.provider_resource_id) diff --git a/llama_stack/providers/utils/inference/litellm_openai_mixin.py b/llama_stack/providers/utils/inference/litellm_openai_mixin.py index efe7031f5..c3c2ab61f 100644 --- a/llama_stack/providers/utils/inference/litellm_openai_mixin.py +++ b/llama_stack/providers/utils/inference/litellm_openai_mixin.py @@ -4,7 +4,8 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union +from collections.abc import AsyncGenerator, AsyncIterator +from typing import Any import litellm @@ -64,7 +65,7 @@ class LiteLLMOpenAIMixin( def __init__( self, model_entries, - api_key_from_config: Optional[str], + api_key_from_config: str | None, provider_data_api_key_field: str, openai_compat_api_base: str | None = None, ): @@ -90,30 +91,33 @@ class LiteLLMOpenAIMixin( raise ValueError(f"Unsupported model: {model.provider_resource_id}") return model + def get_litellm_model_name(self, model_id: str) -> str: + return "openai/" + model_id if self.is_openai_compat else model_id + async def completion( self, model_id: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> AsyncGenerator: raise NotImplementedError("LiteLLM does not support completion requests") async def chat_completion( self, model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, - ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]: + messages: list[Message], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_choice: ToolChoice | None = ToolChoice.auto, + tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, + ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]: if sampling_params is None: sampling_params = SamplingParams() @@ -130,8 +134,7 @@ class LiteLLMOpenAIMixin( ) params = await self._get_params(request) - if self.is_openai_compat: - params["model"] = "openai/" + params["model"] + params["model"] = self.get_litellm_model_name(params["model"]) logger.debug(f"params to litellm (openai compat): {params}") # unfortunately, we need to use synchronous litellm.completion here because litellm @@ -220,34 +223,36 @@ class LiteLLMOpenAIMixin( else request.tool_config.tool_choice ) - provider_data = self.get_request_provider_data() - key_field = self.provider_data_api_key_field - if provider_data and getattr(provider_data, key_field, None): - api_key = getattr(provider_data, key_field) - else: - api_key = self.api_key_from_config - return { "model": request.model, - "api_key": api_key, + "api_key": self.get_api_key(), "api_base": self.api_base, **input_dict, "stream": request.stream, **get_sampling_options(request.sampling_params), } + def get_api_key(self) -> str: + provider_data = self.get_request_provider_data() + key_field = self.provider_data_api_key_field + if provider_data and getattr(provider_data, key_field, None): + api_key = getattr(provider_data, key_field) + else: + api_key = self.api_key_from_config + return api_key + async def embeddings( self, model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: model = await self.model_store.get_model(model_id) response = litellm.embedding( - model=model.provider_resource_id, + model=self.get_litellm_model_name(model.provider_resource_id), input=[interleaved_content_as_str(content) for content in contents], ) @@ -257,28 +262,28 @@ class LiteLLMOpenAIMixin( async def openai_completion( self, model: str, - prompt: Union[str, List[str], List[int], List[List[int]]], - best_of: Optional[int] = None, - echo: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - presence_penalty: Optional[float] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - guided_choice: Optional[List[str]] = None, - prompt_logprobs: Optional[int] = None, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, ) -> OpenAICompletion: model_obj = await self.model_store.get_model(model) params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, + model=self.get_litellm_model_name(model_obj.provider_resource_id), prompt=prompt, best_of=best_of, echo=echo, @@ -297,38 +302,40 @@ class LiteLLMOpenAIMixin( user=user, guided_choice=guided_choice, prompt_logprobs=prompt_logprobs, + api_key=self.get_api_key(), + api_base=self.api_base, ) return await litellm.atext_completion(**params) async def openai_chat_completion( self, model: str, - messages: List[OpenAIMessageParam], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: + messages: list[OpenAIMessageParam], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: model_obj = await self.model_store.get_model(model) params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, + model=self.get_litellm_model_name(model_obj.provider_resource_id), messages=messages, frequency_penalty=frequency_penalty, function_call=function_call, @@ -351,27 +358,29 @@ class LiteLLMOpenAIMixin( top_logprobs=top_logprobs, top_p=top_p, user=user, + api_key=self.get_api_key(), + api_base=self.api_base, ) return await litellm.acompletion(**params) async def batch_completion( self, model_id: str, - content_batch: List[InterleavedContent], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + content_batch: list[InterleavedContent], + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ): raise NotImplementedError("Batch completion is not supported for OpenAI Compat") async def batch_chat_completion( self, model_id: str, - messages_batch: List[List[Message]], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_config: Optional[ToolConfig] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, + messages_batch: list[list[Message]], + sampling_params: SamplingParams | None = None, + tools: list[ToolDefinition] | None = None, + tool_config: ToolConfig | None = None, + response_format: ResponseFormat | None = None, + logprobs: LogProbConfig | None = None, ): raise NotImplementedError("Batch chat completion is not supported for OpenAI Compat") diff --git a/llama_stack/providers/utils/inference/model_registry.py b/llama_stack/providers/utils/inference/model_registry.py index 4d7063953..d707e36c2 100644 --- a/llama_stack/providers/utils/inference/model_registry.py +++ b/llama_stack/providers/utils/inference/model_registry.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict, List, Optional +from typing import Any from pydantic import BaseModel, Field @@ -20,13 +20,13 @@ from llama_stack.providers.utils.inference import ( # more closer to the Model class. class ProviderModelEntry(BaseModel): provider_model_id: str - aliases: List[str] = Field(default_factory=list) - llama_model: Optional[str] = None + aliases: list[str] = Field(default_factory=list) + llama_model: str | None = None model_type: ModelType = ModelType.llm - metadata: Dict[str, Any] = Field(default_factory=dict) + metadata: dict[str, Any] = Field(default_factory=dict) -def get_huggingface_repo(model_descriptor: str) -> Optional[str]: +def get_huggingface_repo(model_descriptor: str) -> str | None: for model in all_registered_models(): if model.descriptor() == model_descriptor: return model.huggingface_repo @@ -34,7 +34,7 @@ def get_huggingface_repo(model_descriptor: str) -> Optional[str]: def build_hf_repo_model_entry( - provider_model_id: str, model_descriptor: str, additional_aliases: Optional[List[str]] = None + provider_model_id: str, model_descriptor: str, additional_aliases: list[str] | None = None ) -> ProviderModelEntry: aliases = [ get_huggingface_repo(model_descriptor), @@ -58,7 +58,7 @@ def build_model_entry(provider_model_id: str, model_descriptor: str) -> Provider class ModelRegistryHelper(ModelsProtocolPrivate): - def __init__(self, model_entries: List[ProviderModelEntry]): + def __init__(self, model_entries: list[ProviderModelEntry]): self.alias_to_provider_id_map = {} self.provider_id_to_llama_model_map = {} for entry in model_entries: @@ -72,43 +72,53 @@ class ModelRegistryHelper(ModelsProtocolPrivate): self.alias_to_provider_id_map[entry.llama_model] = entry.provider_model_id self.provider_id_to_llama_model_map[entry.provider_model_id] = entry.llama_model - def get_provider_model_id(self, identifier: str) -> Optional[str]: + def get_provider_model_id(self, identifier: str) -> str | None: return self.alias_to_provider_id_map.get(identifier, None) - def get_llama_model(self, provider_model_id: str) -> Optional[str]: + # TODO: why keep a separate llama model mapping? + def get_llama_model(self, provider_model_id: str) -> str | None: return self.provider_id_to_llama_model_map.get(provider_model_id, None) async def register_model(self, model: Model) -> Model: + if not (supported_model_id := self.get_provider_model_id(model.provider_resource_id)): + raise ValueError( + f"Model '{model.provider_resource_id}' is not supported. Supported models are: {', '.join(self.alias_to_provider_id_map.keys())}" + ) + provider_resource_id = self.get_provider_model_id(model.model_id) if model.model_type == ModelType.embedding: # embedding models are always registered by their provider model id and does not need to be mapped to a llama model provider_resource_id = model.provider_resource_id - else: - provider_resource_id = self.get_provider_model_id(model.provider_resource_id) - if provider_resource_id: - model.provider_resource_id = provider_resource_id + if provider_resource_id != supported_model_id: # be idemopotent, only reject differences + raise ValueError( + f"Model id '{model.model_id}' is already registered. Please use a different id or unregister it first." + ) else: llama_model = model.metadata.get("llama_model") - if llama_model is None: - return model + if llama_model: + existing_llama_model = self.get_llama_model(model.provider_resource_id) + if existing_llama_model: + if existing_llama_model != llama_model: + raise ValueError( + f"Provider model id '{model.provider_resource_id}' is already registered to a different llama model: '{existing_llama_model}'" + ) + else: + if llama_model not in ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR: + raise ValueError( + f"Invalid llama_model '{llama_model}' specified in metadata. " + f"Must be one of: {', '.join(ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR.keys())}" + ) + self.provider_id_to_llama_model_map[model.provider_resource_id] = ( + ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[llama_model] + ) - existing_llama_model = self.get_llama_model(model.provider_resource_id) - if existing_llama_model: - if existing_llama_model != llama_model: - raise ValueError( - f"Provider model id '{model.provider_resource_id}' is already registered to a different llama model: '{existing_llama_model}'" - ) - else: - if llama_model not in ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR: - raise ValueError( - f"Invalid llama_model '{llama_model}' specified in metadata. " - f"Must be one of: {', '.join(ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR.keys())}" - ) - self.provider_id_to_llama_model_map[model.provider_resource_id] = ( - ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[llama_model] - ) + self.alias_to_provider_id_map[model.model_id] = supported_model_id return model async def unregister_model(self, model_id: str) -> None: - pass + # TODO: should we block unregistering base supported provider model IDs? + if model_id not in self.alias_to_provider_id_map: + raise ValueError(f"Model id '{model_id}' is not registered.") + + del self.alias_to_provider_id_map[model_id] diff --git a/llama_stack/providers/utils/inference/openai_compat.py b/llama_stack/providers/utils/inference/openai_compat.py index d98261abb..e2314d44f 100644 --- a/llama_stack/providers/utils/inference/openai_compat.py +++ b/llama_stack/providers/utils/inference/openai_compat.py @@ -8,7 +8,10 @@ import logging import time import uuid import warnings -from typing import Any, AsyncGenerator, AsyncIterator, Awaitable, Dict, Iterable, List, Optional, Union +from collections.abc import AsyncGenerator, AsyncIterator, Awaitable, Iterable +from typing import ( + Any, +) from openai import AsyncStream from openai.types.chat import ( @@ -78,6 +81,7 @@ from llama_stack.apis.common.content_types import ( TextDelta, ToolCallDelta, ToolCallParseStatus, + _URLOrData, ) from llama_stack.apis.inference import ( ChatCompletionRequest, @@ -93,6 +97,7 @@ from llama_stack.apis.inference import ( SamplingParams, SystemMessage, TokenLogProbs, + ToolChoice, ToolResponseMessage, TopKSamplingStrategy, TopPSamplingStrategy, @@ -130,24 +135,24 @@ class OpenAICompatCompletionChoiceDelta(BaseModel): class OpenAICompatLogprobs(BaseModel): - text_offset: Optional[List[int]] = None + text_offset: list[int] | None = None - token_logprobs: Optional[List[float]] = None + token_logprobs: list[float] | None = None - tokens: Optional[List[str]] = None + tokens: list[str] | None = None - top_logprobs: Optional[List[Dict[str, float]]] = None + top_logprobs: list[dict[str, float]] | None = None class OpenAICompatCompletionChoice(BaseModel): - finish_reason: Optional[str] = None - text: Optional[str] = None - delta: Optional[OpenAICompatCompletionChoiceDelta] = None - logprobs: Optional[OpenAICompatLogprobs] = None + finish_reason: str | None = None + text: str | None = None + delta: OpenAICompatCompletionChoiceDelta | None = None + logprobs: OpenAICompatLogprobs | None = None class OpenAICompatCompletionResponse(BaseModel): - choices: List[OpenAICompatCompletionChoice] + choices: list[OpenAICompatCompletionChoice] def get_sampling_strategy_options(params: SamplingParams) -> dict: @@ -206,8 +211,8 @@ def get_stop_reason(finish_reason: str) -> StopReason: def convert_openai_completion_logprobs( - logprobs: Optional[OpenAICompatLogprobs], -) -> Optional[List[TokenLogProbs]]: + logprobs: OpenAICompatLogprobs | None, +) -> list[TokenLogProbs] | None: if not logprobs: return None if hasattr(logprobs, "top_logprobs"): @@ -224,7 +229,7 @@ def convert_openai_completion_logprobs( return None -def convert_openai_completion_logprobs_stream(text: str, logprobs: Optional[Union[float, OpenAICompatLogprobs]]): +def convert_openai_completion_logprobs_stream(text: str, logprobs: float | OpenAICompatLogprobs | None): if logprobs is None: return None if isinstance(logprobs, float): @@ -513,11 +518,31 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals else: content = [await _convert_content(message.content)] - return { + result = { "role": message.role, "content": content, } + if hasattr(message, "tool_calls") and message.tool_calls: + result["tool_calls"] = [] + for tc in message.tool_calls: + # The tool.tool_name can be a str or a BuiltinTool enum. If + # it's the latter, convert to a string. + tool_name = tc.tool_name + if isinstance(tool_name, BuiltinTool): + tool_name = tool_name.value + result["tool_calls"].append( + { + "id": tc.call_id, + "type": "function", + "function": { + "name": tool_name, + "arguments": tc.arguments_json if hasattr(tc, "arguments_json") else json.dumps(tc.arguments), + }, + } + ) + return result + class UnparseableToolCall(BaseModel): """ @@ -531,7 +556,7 @@ class UnparseableToolCall(BaseModel): async def convert_message_to_openai_dict_new( - message: Message | Dict, + message: Message | dict, ) -> OpenAIChatCompletionMessage: """ Convert a Message to an OpenAI API-compatible dictionary. @@ -560,14 +585,10 @@ async def convert_message_to_openai_dict_new( # List[...] -> List[...] async def _convert_message_content( content: InterleavedContent, - ) -> Union[str, Iterable[OpenAIChatCompletionContentPartParam]]: + ) -> str | Iterable[OpenAIChatCompletionContentPartParam]: async def impl( content_: InterleavedContent, - ) -> Union[ - str, - OpenAIChatCompletionContentPartParam, - List[OpenAIChatCompletionContentPartParam], - ]: + ) -> str | OpenAIChatCompletionContentPartParam | list[OpenAIChatCompletionContentPartParam]: # Llama Stack and OpenAI spec match for str and text input if isinstance(content_, str): return content_ @@ -614,7 +635,7 @@ async def convert_message_to_openai_dict_new( ] params = {} if tool_calls: - params = {"tool_calls": tool_calls} + params["tool_calls"] = tool_calls out = OpenAIChatCompletionAssistantMessage( role="assistant", content=await _convert_message_content(message.content), @@ -639,7 +660,7 @@ async def convert_message_to_openai_dict_new( def convert_tool_call( tool_call: ChatCompletionMessageToolCall, -) -> Union[ToolCall, UnparseableToolCall]: +) -> ToolCall | UnparseableToolCall: """ Convert a ChatCompletionMessageToolCall tool call to either a ToolCall or UnparseableToolCall. Returns an UnparseableToolCall @@ -695,7 +716,10 @@ def to_openai_param_type(param_type: str) -> dict: if param_type.startswith("list[") and param_type.endswith("]"): inner_type = param_type[5:-1] if inner_type in basic_types: - return {"type": "array", "items": {"type": basic_types.get(inner_type, inner_type)}} + return { + "type": "array", + "items": {"type": basic_types.get(inner_type, inner_type)}, + } return {"type": param_type} @@ -812,14 +836,18 @@ def _convert_openai_finish_reason(finish_reason: str) -> StopReason: }.get(finish_reason, StopReason.end_of_turn) -def _convert_openai_request_tool_config(tool_choice: Optional[Union[str, Dict[str, Any]]] = None) -> ToolConfig: +def _convert_openai_request_tool_config(tool_choice: str | dict[str, Any] | None = None) -> ToolConfig: tool_config = ToolConfig() if tool_choice: + try: + tool_choice = ToolChoice(tool_choice) + except ValueError: + pass tool_config.tool_choice = tool_choice return tool_config -def _convert_openai_request_tools(tools: Optional[List[Dict[str, Any]]] = None) -> List[ToolDefinition]: +def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) -> list[ToolDefinition]: lls_tools = [] if not tools: return lls_tools @@ -835,7 +863,7 @@ def _convert_openai_request_tools(tools: Optional[List[Dict[str, Any]]] = None) tool_param_properties = tool_params.get("properties", {}) for tool_param_key, tool_param_value in tool_param_properties.items(): tool_param_def = ToolParamDefinition( - param_type=tool_param_value.get("type", None), + param_type=str(tool_param_value.get("type", None)), description=tool_param_value.get("description", None), ) lls_tool_params[tool_param_key] = tool_param_def @@ -849,7 +877,9 @@ def _convert_openai_request_tools(tools: Optional[List[Dict[str, Any]]] = None) return lls_tools -def _convert_openai_request_response_format(response_format: OpenAIResponseFormatParam = None): +def _convert_openai_request_response_format( + response_format: OpenAIResponseFormatParam = None, +): if not response_format: return None # response_format can be a dict or a pydantic model @@ -863,8 +893,8 @@ def _convert_openai_request_response_format(response_format: OpenAIResponseForma def _convert_openai_tool_calls( - tool_calls: List[OpenAIChatCompletionMessageToolCall], -) -> List[ToolCall]: + tool_calls: list[OpenAIChatCompletionMessageToolCall], +) -> list[ToolCall]: """ Convert an OpenAI ChatCompletionMessageToolCall list into a list of ToolCall. @@ -900,7 +930,7 @@ def _convert_openai_tool_calls( def _convert_openai_logprobs( logprobs: OpenAIChoiceLogprobs, -) -> Optional[List[TokenLogProbs]]: +) -> list[TokenLogProbs] | None: """ Convert an OpenAI ChoiceLogprobs into a list of TokenLogProbs. @@ -933,9 +963,9 @@ def _convert_openai_logprobs( def _convert_openai_sampling_params( - max_tokens: Optional[int] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, + max_tokens: int | None = None, + temperature: float | None = None, + top_p: float | None = None, ) -> SamplingParams: sampling_params = SamplingParams() @@ -957,38 +987,50 @@ def _convert_openai_sampling_params( return sampling_params -def _convert_openai_request_messages(messages: List[OpenAIMessageParam]): - # Llama Stack messages and OpenAI messages are similar, but not identical. - lls_messages = [] +def openai_messages_to_messages( + messages: list[OpenAIMessageParam], +) -> list[Message]: + """ + Convert a list of OpenAIChatCompletionMessage into a list of Message. + """ + converted_messages = [] for message in messages: - lls_message = dict(message) + if message.role == "system": + converted_message = SystemMessage(content=openai_content_to_content(message.content)) + elif message.role == "user": + converted_message = UserMessage(content=openai_content_to_content(message.content)) + elif message.role == "assistant": + converted_message = CompletionMessage( + content=openai_content_to_content(message.content), + tool_calls=_convert_openai_tool_calls(message.tool_calls), + stop_reason=StopReason.end_of_turn, + ) + elif message.role == "tool": + converted_message = ToolResponseMessage( + role="tool", + call_id=message.tool_call_id, + content=openai_content_to_content(message.content), + ) + else: + raise ValueError(f"Unknown role {message.role}") + converted_messages.append(converted_message) + return converted_messages - # Llama Stack expects `call_id` but OpenAI uses `tool_call_id` - tool_call_id = lls_message.pop("tool_call_id", None) - if tool_call_id: - lls_message["call_id"] = tool_call_id - content = lls_message.get("content", None) - if isinstance(content, list): - lls_content = [] - for item in content: - # items can either by pydantic models or dicts here... - item = dict(item) - if item.get("type", "") == "image_url": - lls_item = ImageContentItem( - type="image", - image=URL(uri=item.get("image_url", {}).get("url", "")), - ) - elif item.get("type", "") == "text": - lls_item = TextContentItem( - type="text", - text=item.get("text", ""), - ) - lls_content.append(lls_item) - lls_message["content"] = lls_content - lls_messages.append(lls_message) - - return lls_messages +def openai_content_to_content(content: str | Iterable[OpenAIChatCompletionContentPartParam]): + if isinstance(content, str): + return content + elif isinstance(content, list): + return [openai_content_to_content(c) for c in content] + elif hasattr(content, "type"): + if content.type == "text": + return TextContentItem(type="text", text=content.text) + elif content.type == "image_url": + return ImageContentItem(type="image", image=_URLOrData(url=URL(uri=content.image_url.url))) + else: + raise ValueError(f"Unknown content type: {content.type}") + else: + raise ValueError(f"Unknown content type: {content}") def convert_openai_chat_completion_choice( @@ -1221,24 +1263,24 @@ class OpenAICompletionToLlamaStackMixin: async def openai_completion( self, model: str, - prompt: Union[str, List[str], List[int], List[List[int]]], - best_of: Optional[int] = None, - echo: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - presence_penalty: Optional[float] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - guided_choice: Optional[List[str]] = None, - prompt_logprobs: Optional[int] = None, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, ) -> OpenAICompletion: if stream: raise ValueError(f"{self.__class__.__name__} doesn't support streaming openai completions") @@ -1290,30 +1332,30 @@ class OpenAIChatCompletionToLlamaStackMixin: async def openai_chat_completion( self, model: str, - messages: List[OpenAIChatCompletionMessage], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: - messages = _convert_openai_request_messages(messages) + messages: list[OpenAIMessageParam], + frequency_penalty: float | None = None, + function_call: str | dict[str, Any] | None = None, + functions: list[dict[str, Any]] | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_completion_tokens: int | None = None, + max_tokens: int | None = None, + n: int | None = None, + parallel_tool_calls: bool | None = None, + presence_penalty: float | None = None, + response_format: OpenAIResponseFormatParam | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + tool_choice: str | dict[str, Any] | None = None, + tools: list[dict[str, Any]] | None = None, + top_logprobs: int | None = None, + top_p: float | None = None, + user: str | None = None, + ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: + messages = openai_messages_to_messages(messages) response_format = _convert_openai_request_response_format(response_format) sampling_params = _convert_openai_sampling_params( max_tokens=max_tokens, @@ -1321,7 +1363,10 @@ class OpenAIChatCompletionToLlamaStackMixin: top_p=top_p, ) tool_config = _convert_openai_request_tool_config(tool_choice) + tools = _convert_openai_request_tools(tools) + if tool_config.tool_choice == ToolChoice.none: + tools = [] outstanding_responses = [] # "n" is the number of completions to generate per prompt @@ -1346,7 +1391,9 @@ class OpenAIChatCompletionToLlamaStackMixin: ) async def _process_stream_response( - self, model: str, outstanding_responses: List[Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]] + self, + model: str, + outstanding_responses: list[Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]], ): id = f"chatcmpl-{uuid.uuid4()}" for outstanding_response in outstanding_responses: @@ -1369,11 +1416,31 @@ class OpenAIChatCompletionToLlamaStackMixin: elif isinstance(event.delta, ToolCallDelta): if event.delta.parse_status == ToolCallParseStatus.succeeded: tool_call = event.delta.tool_call + + # First chunk includes full structure openai_tool_call = OpenAIChoiceDeltaToolCall( index=0, id=tool_call.call_id, function=OpenAIChoiceDeltaToolCallFunction( - name=tool_call.tool_name, arguments=tool_call.arguments_json + name=tool_call.tool_name, + arguments="", + ), + ) + delta = OpenAIChoiceDelta(tool_calls=[openai_tool_call]) + yield OpenAIChatCompletionChunk( + id=id, + choices=[ + OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta) + ], + created=int(time.time()), + model=model, + object="chat.completion.chunk", + ) + # arguments + openai_tool_call = OpenAIChoiceDeltaToolCall( + index=0, + function=OpenAIChoiceDeltaToolCallFunction( + arguments=tool_call.arguments_json, ), ) delta = OpenAIChoiceDelta(tool_calls=[openai_tool_call]) @@ -1389,7 +1456,7 @@ class OpenAIChatCompletionToLlamaStackMixin: i = i + 1 async def _process_non_stream_response( - self, model: str, outstanding_responses: List[Awaitable[ChatCompletionResponse]] + self, model: str, outstanding_responses: list[Awaitable[ChatCompletionResponse]] ) -> OpenAIChatCompletion: choices = [] for outstanding_response in outstanding_responses: diff --git a/llama_stack/providers/utils/inference/prompt_adapter.py b/llama_stack/providers/utils/inference/prompt_adapter.py index 4f9c4927a..d53b51537 100644 --- a/llama_stack/providers/utils/inference/prompt_adapter.py +++ b/llama_stack/providers/utils/inference/prompt_adapter.py @@ -9,7 +9,6 @@ import base64 import io import json import re -from typing import List, Optional, Tuple, Union import httpx from PIL import Image as PIL_Image @@ -52,6 +51,9 @@ from llama_stack.models.llama.llama3.prompt_templates import ( SystemDefaultGenerator, ) from llama_stack.models.llama.llama3.tokenizer import Tokenizer +from llama_stack.models.llama.llama4.prompt_templates.system_prompts import ( + PythonListCustomToolGenerator as PythonListCustomToolGeneratorLlama4, +) from llama_stack.models.llama.sku_list import resolve_model from llama_stack.models.llama.sku_types import ModelFamily, is_multimodal from llama_stack.providers.utils.inference import supported_inference_models @@ -60,7 +62,7 @@ log = get_logger(name=__name__, category="inference") class ChatCompletionRequestWithRawContent(ChatCompletionRequest): - messages: List[RawMessage] + messages: list[RawMessage] class CompletionRequestWithRawContent(CompletionRequest): @@ -90,8 +92,8 @@ def interleaved_content_as_str(content: InterleavedContent, sep: str = " ") -> s async def convert_request_to_raw( - request: Union[ChatCompletionRequest, CompletionRequest], -) -> Union[ChatCompletionRequestWithRawContent, CompletionRequestWithRawContent]: + request: ChatCompletionRequest | CompletionRequest, +) -> ChatCompletionRequestWithRawContent | CompletionRequestWithRawContent: if isinstance(request, ChatCompletionRequest): messages = [] for m in request.messages: @@ -167,18 +169,18 @@ def content_has_media(content: InterleavedContent): return _has_media_content(content) -def messages_have_media(messages: List[Message]): +def messages_have_media(messages: list[Message]): return any(content_has_media(m.content) for m in messages) -def request_has_media(request: Union[ChatCompletionRequest, CompletionRequest]): +def request_has_media(request: ChatCompletionRequest | CompletionRequest): if isinstance(request, ChatCompletionRequest): return messages_have_media(request.messages) else: return content_has_media(request.content) -async def localize_image_content(media: ImageContentItem) -> Tuple[bytes, str]: +async def localize_image_content(media: ImageContentItem) -> tuple[bytes, str]: image = media.image if image.url and image.url.uri.startswith("http"): async with httpx.AsyncClient() as client: @@ -225,7 +227,7 @@ async def completion_request_to_prompt(request: CompletionRequest) -> str: async def completion_request_to_prompt_model_input_info( request: CompletionRequest, -) -> Tuple[str, int]: +) -> tuple[str, int]: content = augment_content_with_response_format_prompt(request.response_format, request.content) request.content = content request = await convert_request_to_raw(request) @@ -262,7 +264,7 @@ async def chat_completion_request_to_prompt(request: ChatCompletionRequest, llam async def chat_completion_request_to_model_input_info( request: ChatCompletionRequest, llama_model: str -) -> Tuple[str, int]: +) -> tuple[str, int]: messages = chat_completion_request_to_messages(request, llama_model) request.messages = messages request = await convert_request_to_raw(request) @@ -281,7 +283,7 @@ async def chat_completion_request_to_model_input_info( def chat_completion_request_to_messages( request: ChatCompletionRequest, llama_model: str, -) -> List[Message]: +) -> list[Message]: """Reads chat completion request and augments the messages to handle tools. For eg. for llama_3_1, add system message with the appropriate tools or add user messsage for custom tools, etc. @@ -306,10 +308,11 @@ def chat_completion_request_to_messages( elif model.model_family in ( ModelFamily.llama3_2, ModelFamily.llama3_3, - ModelFamily.llama4, ): - # llama3.2, llama3.3 and llama4 models follow the same tool prompt format - messages = augment_messages_for_tools_llama_3_2(request) + # llama3.2, llama3.3 follow the same tool prompt format + messages = augment_messages_for_tools_llama(request, PythonListCustomToolGenerator) + elif model.model_family == ModelFamily.llama4: + messages = augment_messages_for_tools_llama(request, PythonListCustomToolGeneratorLlama4) else: messages = request.messages @@ -319,7 +322,7 @@ def chat_completion_request_to_messages( return messages -def response_format_prompt(fmt: Optional[ResponseFormat]): +def response_format_prompt(fmt: ResponseFormat | None): if not fmt: return None @@ -333,7 +336,7 @@ def response_format_prompt(fmt: Optional[ResponseFormat]): def augment_messages_for_tools_llama_3_1( request: ChatCompletionRequest, -) -> List[Message]: +) -> list[Message]: existing_messages = request.messages existing_system_message = None if existing_messages[0].role == Role.system.value: @@ -399,9 +402,10 @@ def augment_messages_for_tools_llama_3_1( return messages -def augment_messages_for_tools_llama_3_2( +def augment_messages_for_tools_llama( request: ChatCompletionRequest, -) -> List[Message]: + custom_tool_prompt_generator, +) -> list[Message]: existing_messages = request.messages existing_system_message = None if existing_messages[0].role == Role.system.value: @@ -434,7 +438,7 @@ def augment_messages_for_tools_llama_3_2( if existing_system_message and request.tool_config.system_message_behavior == SystemMessageBehavior.replace: system_prompt = existing_system_message.content - tool_template = PythonListCustomToolGenerator().gen(custom_tools, system_prompt) + tool_template = custom_tool_prompt_generator().gen(custom_tools, system_prompt) sys_content += tool_template.render() sys_content += "\n" @@ -452,7 +456,7 @@ def augment_messages_for_tools_llama_3_2( return messages -def _get_tool_choice_prompt(tool_choice: ToolChoice | str, tools: List[ToolDefinition]) -> str: +def _get_tool_choice_prompt(tool_choice: ToolChoice | str, tools: list[ToolDefinition]) -> str: if tool_choice == ToolChoice.auto: return "" elif tool_choice == ToolChoice.required: diff --git a/llama_stack/providers/utils/kvstore/api.py b/llama_stack/providers/utils/kvstore/api.py index 84b1730e1..8efde4ea9 100644 --- a/llama_stack/providers/utils/kvstore/api.py +++ b/llama_stack/providers/utils/kvstore/api.py @@ -5,15 +5,15 @@ # the root directory of this source tree. from datetime import datetime -from typing import List, Optional, Protocol +from typing import Protocol class KVStore(Protocol): # TODO: make the value type bytes instead of str - async def set(self, key: str, value: str, expiration: Optional[datetime] = None) -> None: ... + async def set(self, key: str, value: str, expiration: datetime | None = None) -> None: ... - async def get(self, key: str) -> Optional[str]: ... + async def get(self, key: str) -> str | None: ... async def delete(self, key: str) -> None: ... - async def range(self, start_key: str, end_key: str) -> List[str]: ... + async def range(self, start_key: str, end_key: str) -> list[str]: ... diff --git a/llama_stack/providers/utils/kvstore/config.py b/llama_stack/providers/utils/kvstore/config.py index 4f85982be..e9aac6e8c 100644 --- a/llama_stack/providers/utils/kvstore/config.py +++ b/llama_stack/providers/utils/kvstore/config.py @@ -6,10 +6,9 @@ import re from enum import Enum -from typing import Literal, Optional, Union +from typing import Annotated, Literal from pydantic import BaseModel, Field, field_validator -from typing_extensions import Annotated from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR @@ -22,7 +21,7 @@ class KVStoreType(Enum): class CommonConfig(BaseModel): - namespace: Optional[str] = Field( + namespace: str | None = Field( default=None, description="All keys will be prefixed with this namespace", ) @@ -69,7 +68,7 @@ class PostgresKVStoreConfig(CommonConfig): port: int = 5432 db: str = "llamastack" user: str - password: Optional[str] = None + password: str | None = None table_name: str = "llamastack_kvstore" @classmethod @@ -108,7 +107,7 @@ class MongoDBKVStoreConfig(CommonConfig): port: int = 27017 db: str = "llamastack" user: str = None - password: Optional[str] = None + password: str | None = None collection_name: str = "llamastack_kvstore" @classmethod @@ -126,6 +125,6 @@ class MongoDBKVStoreConfig(CommonConfig): KVStoreConfig = Annotated[ - Union[RedisKVStoreConfig, SqliteKVStoreConfig, PostgresKVStoreConfig, MongoDBKVStoreConfig], + RedisKVStoreConfig | SqliteKVStoreConfig | PostgresKVStoreConfig | MongoDBKVStoreConfig, Field(discriminator="type", default=KVStoreType.sqlite.value), ] diff --git a/llama_stack/providers/utils/kvstore/kvstore.py b/llama_stack/providers/utils/kvstore/kvstore.py index 6bc175260..0eb969b65 100644 --- a/llama_stack/providers/utils/kvstore/kvstore.py +++ b/llama_stack/providers/utils/kvstore/kvstore.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List, Optional from .api import KVStore from .config import KVStoreConfig, KVStoreType @@ -21,13 +20,13 @@ class InmemoryKVStoreImpl(KVStore): async def initialize(self) -> None: pass - async def get(self, key: str) -> Optional[str]: + async def get(self, key: str) -> str | None: return self._store.get(key) async def set(self, key: str, value: str) -> None: self._store[key] = value - async def range(self, start_key: str, end_key: str) -> List[str]: + async def range(self, start_key: str, end_key: str) -> list[str]: return [self._store[key] for key in self._store.keys() if key >= start_key and key < end_key] diff --git a/llama_stack/providers/utils/kvstore/mongodb/mongodb.py b/llama_stack/providers/utils/kvstore/mongodb/mongodb.py index c1581dc8d..330a079bf 100644 --- a/llama_stack/providers/utils/kvstore/mongodb/mongodb.py +++ b/llama_stack/providers/utils/kvstore/mongodb/mongodb.py @@ -6,7 +6,6 @@ import logging from datetime import datetime -from typing import List, Optional from pymongo import AsyncMongoClient @@ -43,12 +42,12 @@ class MongoDBKVStoreImpl(KVStore): return key return f"{self.config.namespace}:{key}" - async def set(self, key: str, value: str, expiration: Optional[datetime] = None) -> None: + async def set(self, key: str, value: str, expiration: datetime | None = None) -> None: key = self._namespaced_key(key) update_query = {"$set": {"value": value, "expiration": expiration}} await self.collection.update_one({"key": key}, update_query, upsert=True) - async def get(self, key: str) -> Optional[str]: + async def get(self, key: str) -> str | None: key = self._namespaced_key(key) query = {"key": key} result = await self.collection.find_one(query, {"value": 1, "_id": 0}) @@ -58,7 +57,7 @@ class MongoDBKVStoreImpl(KVStore): key = self._namespaced_key(key) await self.collection.delete_one({"key": key}) - async def range(self, start_key: str, end_key: str) -> List[str]: + async def range(self, start_key: str, end_key: str) -> list[str]: start_key = self._namespaced_key(start_key) end_key = self._namespaced_key(end_key) query = { diff --git a/llama_stack/providers/utils/kvstore/postgres/postgres.py b/llama_stack/providers/utils/kvstore/postgres/postgres.py index 097d36066..6bfbc4f81 100644 --- a/llama_stack/providers/utils/kvstore/postgres/postgres.py +++ b/llama_stack/providers/utils/kvstore/postgres/postgres.py @@ -6,7 +6,6 @@ import logging from datetime import datetime -from typing import List, Optional import psycopg2 from psycopg2.extras import DictCursor @@ -54,7 +53,7 @@ class PostgresKVStoreImpl(KVStore): return key return f"{self.config.namespace}:{key}" - async def set(self, key: str, value: str, expiration: Optional[datetime] = None) -> None: + async def set(self, key: str, value: str, expiration: datetime | None = None) -> None: key = self._namespaced_key(key) self.cursor.execute( f""" @@ -66,7 +65,7 @@ class PostgresKVStoreImpl(KVStore): (key, value, expiration), ) - async def get(self, key: str) -> Optional[str]: + async def get(self, key: str) -> str | None: key = self._namespaced_key(key) self.cursor.execute( f""" @@ -86,7 +85,7 @@ class PostgresKVStoreImpl(KVStore): (key,), ) - async def range(self, start_key: str, end_key: str) -> List[str]: + async def range(self, start_key: str, end_key: str) -> list[str]: start_key = self._namespaced_key(start_key) end_key = self._namespaced_key(end_key) diff --git a/llama_stack/providers/utils/kvstore/redis/redis.py b/llama_stack/providers/utils/kvstore/redis/redis.py index a390ea866..d95de0291 100644 --- a/llama_stack/providers/utils/kvstore/redis/redis.py +++ b/llama_stack/providers/utils/kvstore/redis/redis.py @@ -5,7 +5,6 @@ # the root directory of this source tree. from datetime import datetime -from typing import List, Optional from redis.asyncio import Redis @@ -25,13 +24,13 @@ class RedisKVStoreImpl(KVStore): return key return f"{self.config.namespace}:{key}" - async def set(self, key: str, value: str, expiration: Optional[datetime] = None) -> None: + async def set(self, key: str, value: str, expiration: datetime | None = None) -> None: key = self._namespaced_key(key) await self.redis.set(key, value) if expiration: await self.redis.expireat(key, expiration) - async def get(self, key: str) -> Optional[str]: + async def get(self, key: str) -> str | None: key = self._namespaced_key(key) value = await self.redis.get(key) if value is None: @@ -43,7 +42,7 @@ class RedisKVStoreImpl(KVStore): key = self._namespaced_key(key) await self.redis.delete(key) - async def range(self, start_key: str, end_key: str) -> List[str]: + async def range(self, start_key: str, end_key: str) -> list[str]: start_key = self._namespaced_key(start_key) end_key = self._namespaced_key(end_key) cursor = 0 diff --git a/llama_stack/providers/utils/kvstore/sqlite/sqlite.py b/llama_stack/providers/utils/kvstore/sqlite/sqlite.py index bc0488aac..faca77887 100644 --- a/llama_stack/providers/utils/kvstore/sqlite/sqlite.py +++ b/llama_stack/providers/utils/kvstore/sqlite/sqlite.py @@ -6,7 +6,6 @@ import os from datetime import datetime -from typing import List, Optional import aiosqlite @@ -33,7 +32,7 @@ class SqliteKVStoreImpl(KVStore): ) await db.commit() - async def set(self, key: str, value: str, expiration: Optional[datetime] = None) -> None: + async def set(self, key: str, value: str, expiration: datetime | None = None) -> None: async with aiosqlite.connect(self.db_path) as db: await db.execute( f"INSERT OR REPLACE INTO {self.table_name} (key, value, expiration) VALUES (?, ?, ?)", @@ -41,7 +40,7 @@ class SqliteKVStoreImpl(KVStore): ) await db.commit() - async def get(self, key: str) -> Optional[str]: + async def get(self, key: str) -> str | None: async with aiosqlite.connect(self.db_path) as db: async with db.execute(f"SELECT value, expiration FROM {self.table_name} WHERE key = ?", (key,)) as cursor: row = await cursor.fetchone() @@ -55,7 +54,7 @@ class SqliteKVStoreImpl(KVStore): await db.execute(f"DELETE FROM {self.table_name} WHERE key = ?", (key,)) await db.commit() - async def range(self, start_key: str, end_key: str) -> List[str]: + async def range(self, start_key: str, end_key: str) -> list[str]: async with aiosqlite.connect(self.db_path) as db: async with db.execute( f"SELECT key, value, expiration FROM {self.table_name} WHERE key >= ? AND key <= ?", diff --git a/llama_stack/providers/utils/memory/vector_store.py b/llama_stack/providers/utils/memory/vector_store.py index ba4403ea1..f4834969a 100644 --- a/llama_stack/providers/utils/memory/vector_store.py +++ b/llama_stack/providers/utils/memory/vector_store.py @@ -9,7 +9,7 @@ import logging import re from abc import ABC, abstractmethod from dataclasses import dataclass -from typing import Any, Dict, List, Optional +from typing import Any from urllib.parse import unquote import httpx @@ -94,7 +94,7 @@ def content_from_data(data_url: str) -> str: return "" -def concat_interleaved_content(content: List[InterleavedContent]) -> InterleavedContent: +def concat_interleaved_content(content: list[InterleavedContent]) -> InterleavedContent: """concatenate interleaved content into a single list. ensure that 'str's are converted to TextContentItem when in a list""" ret = [] @@ -141,7 +141,7 @@ async def content_from_doc(doc: RAGDocument) -> str: return interleaved_content_as_str(doc.content) -def make_overlapped_chunks(document_id: str, text: str, window_len: int, overlap_len: int) -> List[Chunk]: +def make_overlapped_chunks(document_id: str, text: str, window_len: int, overlap_len: int) -> list[Chunk]: tokenizer = Tokenizer.get_instance() tokens = tokenizer.encode(text, bos=False, eos=False) @@ -165,7 +165,7 @@ def make_overlapped_chunks(document_id: str, text: str, window_len: int, overlap class EmbeddingIndex(ABC): @abstractmethod - async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray): + async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): raise NotImplementedError() @abstractmethod @@ -185,7 +185,7 @@ class VectorDBWithIndex: async def insert_chunks( self, - chunks: List[Chunk], + chunks: list[Chunk], ) -> None: embeddings_response = await self.inference_api.embeddings( self.vector_db.embedding_model, [x.content for x in chunks] @@ -197,7 +197,7 @@ class VectorDBWithIndex: async def query_chunks( self, query: InterleavedContent, - params: Optional[Dict[str, Any]] = None, + params: dict[str, Any] | None = None, ) -> QueryChunksResponse: if params is None: params = {} diff --git a/llama_stack/providers/utils/scheduler.py b/llama_stack/providers/utils/scheduler.py index d4cffe605..845ab1f02 100644 --- a/llama_stack/providers/utils/scheduler.py +++ b/llama_stack/providers/utils/scheduler.py @@ -8,9 +8,10 @@ import abc import asyncio import functools import threading +from collections.abc import Callable, Coroutine, Iterable from datetime import datetime, timezone from enum import Enum -from typing import Any, Callable, Coroutine, Dict, Iterable, Tuple, TypeAlias +from typing import Any, TypeAlias from pydantic import BaseModel @@ -38,7 +39,7 @@ class JobArtifact(BaseModel): name: str # TODO: uri should be a reference to /files API; revisit when /files is implemented uri: str | None = None - metadata: Dict[str, Any] + metadata: dict[str, Any] JobHandler = Callable[ @@ -46,7 +47,7 @@ JobHandler = Callable[ ] -LogMessage: TypeAlias = Tuple[datetime, str] +LogMessage: TypeAlias = tuple[datetime, str] _COMPLETED_STATUSES = {JobStatus.completed, JobStatus.failed} @@ -60,7 +61,7 @@ class Job: self._handler = handler self._artifacts: list[JobArtifact] = [] self._logs: list[LogMessage] = [] - self._state_transitions: list[Tuple[datetime, JobStatus]] = [(datetime.now(timezone.utc), JobStatus.new)] + self._state_transitions: list[tuple[datetime, JobStatus]] = [(datetime.now(timezone.utc), JobStatus.new)] @property def handler(self) -> JobHandler: diff --git a/llama_stack/providers/utils/scoring/aggregation_utils.py b/llama_stack/providers/utils/scoring/aggregation_utils.py index 7254c9433..cff9a112f 100644 --- a/llama_stack/providers/utils/scoring/aggregation_utils.py +++ b/llama_stack/providers/utils/scoring/aggregation_utils.py @@ -4,13 +4,13 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. import statistics -from typing import Any, Dict, List +from typing import Any from llama_stack.apis.scoring import ScoringResultRow from llama_stack.apis.scoring_functions import AggregationFunctionType -def aggregate_accuracy(scoring_results: List[ScoringResultRow]) -> Dict[str, Any]: +def aggregate_accuracy(scoring_results: list[ScoringResultRow]) -> dict[str, Any]: num_correct = sum(result["score"] for result in scoring_results) avg_score = num_correct / len(scoring_results) @@ -21,14 +21,14 @@ def aggregate_accuracy(scoring_results: List[ScoringResultRow]) -> Dict[str, Any } -def aggregate_average(scoring_results: List[ScoringResultRow]) -> Dict[str, Any]: +def aggregate_average(scoring_results: list[ScoringResultRow]) -> dict[str, Any]: return { "average": sum(result["score"] for result in scoring_results if result["score"] is not None) / len([_ for _ in scoring_results if _["score"] is not None]), } -def aggregate_weighted_average(scoring_results: List[ScoringResultRow]) -> Dict[str, Any]: +def aggregate_weighted_average(scoring_results: list[ScoringResultRow]) -> dict[str, Any]: return { "weighted_average": sum( result["score"] * result["weight"] @@ -40,14 +40,14 @@ def aggregate_weighted_average(scoring_results: List[ScoringResultRow]) -> Dict[ def aggregate_categorical_count( - scoring_results: List[ScoringResultRow], -) -> Dict[str, Any]: + scoring_results: list[ScoringResultRow], +) -> dict[str, Any]: scores = [str(r["score"]) for r in scoring_results] unique_scores = sorted(set(scores)) return {"categorical_count": {s: scores.count(s) for s in unique_scores}} -def aggregate_median(scoring_results: List[ScoringResultRow]) -> Dict[str, Any]: +def aggregate_median(scoring_results: list[ScoringResultRow]) -> dict[str, Any]: scores = [r["score"] for r in scoring_results if r["score"] is not None] median = statistics.median(scores) if scores else None return {"median": median} @@ -64,8 +64,8 @@ AGGREGATION_FUNCTIONS = { def aggregate_metrics( - scoring_results: List[ScoringResultRow], metrics: List[AggregationFunctionType] -) -> Dict[str, Any]: + scoring_results: list[ScoringResultRow], metrics: list[AggregationFunctionType] +) -> dict[str, Any]: agg_results = {} for metric in metrics: if metric not in AGGREGATION_FUNCTIONS: diff --git a/llama_stack/providers/utils/scoring/base_scoring_fn.py b/llama_stack/providers/utils/scoring/base_scoring_fn.py index 834deb7e1..2fae177b7 100644 --- a/llama_stack/providers/utils/scoring/base_scoring_fn.py +++ b/llama_stack/providers/utils/scoring/base_scoring_fn.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. from abc import ABC, abstractmethod -from typing import Any, Dict, List, Optional +from typing import Any from llama_stack.apis.scoring import ScoringFnParams, ScoringResultRow from llama_stack.apis.scoring_functions import ScoringFn @@ -28,28 +28,28 @@ class BaseScoringFn(ABC): @abstractmethod async def score_row( self, - input_row: Dict[str, Any], - scoring_fn_identifier: Optional[str] = None, - scoring_params: Optional[ScoringFnParams] = None, + input_row: dict[str, Any], + scoring_fn_identifier: str | None = None, + scoring_params: ScoringFnParams | None = None, ) -> ScoringResultRow: raise NotImplementedError() @abstractmethod async def aggregate( self, - scoring_results: List[ScoringResultRow], - scoring_fn_identifier: Optional[str] = None, - scoring_params: Optional[ScoringFnParams] = None, - ) -> Dict[str, Any]: + scoring_results: list[ScoringResultRow], + scoring_fn_identifier: str | None = None, + scoring_params: ScoringFnParams | None = None, + ) -> dict[str, Any]: raise NotImplementedError() @abstractmethod async def score( self, - input_rows: List[Dict[str, Any]], - scoring_fn_identifier: Optional[str] = None, - scoring_params: Optional[ScoringFnParams] = None, - ) -> List[ScoringResultRow]: + input_rows: list[dict[str, Any]], + scoring_fn_identifier: str | None = None, + scoring_params: ScoringFnParams | None = None, + ) -> list[ScoringResultRow]: raise NotImplementedError() @@ -65,7 +65,7 @@ class RegisteredBaseScoringFn(BaseScoringFn): def __str__(self) -> str: return self.__class__.__name__ - def get_supported_scoring_fn_defs(self) -> List[ScoringFn]: + def get_supported_scoring_fn_defs(self) -> list[ScoringFn]: return list(self.supported_fn_defs_registry.values()) def register_scoring_fn_def(self, scoring_fn: ScoringFn) -> None: @@ -81,18 +81,18 @@ class RegisteredBaseScoringFn(BaseScoringFn): @abstractmethod async def score_row( self, - input_row: Dict[str, Any], - scoring_fn_identifier: Optional[str] = None, - scoring_params: Optional[ScoringFnParams] = None, + input_row: dict[str, Any], + scoring_fn_identifier: str | None = None, + scoring_params: ScoringFnParams | None = None, ) -> ScoringResultRow: raise NotImplementedError() async def aggregate( self, - scoring_results: List[ScoringResultRow], - scoring_fn_identifier: Optional[str] = None, - scoring_params: Optional[ScoringFnParams] = None, - ) -> Dict[str, Any]: + scoring_results: list[ScoringResultRow], + scoring_fn_identifier: str | None = None, + scoring_params: ScoringFnParams | None = None, + ) -> dict[str, Any]: params = self.supported_fn_defs_registry[scoring_fn_identifier].params if scoring_params is not None: if params is None: @@ -107,8 +107,8 @@ class RegisteredBaseScoringFn(BaseScoringFn): async def score( self, - input_rows: List[Dict[str, Any]], - scoring_fn_identifier: Optional[str] = None, - scoring_params: Optional[ScoringFnParams] = None, - ) -> List[ScoringResultRow]: + input_rows: list[dict[str, Any]], + scoring_fn_identifier: str | None = None, + scoring_params: ScoringFnParams | None = None, + ) -> list[ScoringResultRow]: return [await self.score_row(input_row, scoring_fn_identifier, scoring_params) for input_row in input_rows] diff --git a/llama_stack/providers/utils/scoring/basic_scoring_utils.py b/llama_stack/providers/utils/scoring/basic_scoring_utils.py index 91abfdb2e..7372a521c 100644 --- a/llama_stack/providers/utils/scoring/basic_scoring_utils.py +++ b/llama_stack/providers/utils/scoring/basic_scoring_utils.py @@ -5,8 +5,8 @@ # the root directory of this source tree. import contextlib import signal +from collections.abc import Iterator from types import FrameType -from typing import Iterator, Optional class TimeoutError(Exception): @@ -15,7 +15,7 @@ class TimeoutError(Exception): @contextlib.contextmanager def time_limit(seconds: float) -> Iterator[None]: - def signal_handler(signum: int, frame: Optional[FrameType]) -> None: + def signal_handler(signum: int, frame: FrameType | None) -> None: raise TimeoutError("Timed out!") signal.setitimer(signal.ITIMER_REAL, seconds) diff --git a/llama_stack/providers/utils/telemetry/dataset_mixin.py b/llama_stack/providers/utils/telemetry/dataset_mixin.py index 34c612133..fe729a244 100644 --- a/llama_stack/providers/utils/telemetry/dataset_mixin.py +++ b/llama_stack/providers/utils/telemetry/dataset_mixin.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List, Optional from llama_stack.apis.datasetio import DatasetIO from llama_stack.apis.telemetry import QueryCondition, QuerySpansResponse, Span @@ -17,10 +16,10 @@ class TelemetryDatasetMixin: async def save_spans_to_dataset( self, - attribute_filters: List[QueryCondition], - attributes_to_save: List[str], + attribute_filters: list[QueryCondition], + attributes_to_save: list[str], dataset_id: str, - max_depth: Optional[int] = None, + max_depth: int | None = None, ) -> None: if self.datasetio_api is None: raise RuntimeError("DatasetIO API not available") @@ -48,9 +47,9 @@ class TelemetryDatasetMixin: async def query_spans( self, - attribute_filters: List[QueryCondition], - attributes_to_return: List[str], - max_depth: Optional[int] = None, + attribute_filters: list[QueryCondition], + attributes_to_return: list[str], + max_depth: int | None = None, ) -> QuerySpansResponse: traces = await self.query_traces(attribute_filters=attribute_filters) spans = [] diff --git a/llama_stack/providers/utils/telemetry/sqlite_trace_store.py b/llama_stack/providers/utils/telemetry/sqlite_trace_store.py index 3248f3fa7..af1145fe7 100644 --- a/llama_stack/providers/utils/telemetry/sqlite_trace_store.py +++ b/llama_stack/providers/utils/telemetry/sqlite_trace_store.py @@ -6,7 +6,7 @@ import json from datetime import datetime -from typing import Dict, List, Optional, Protocol +from typing import Protocol import aiosqlite @@ -16,18 +16,18 @@ from llama_stack.apis.telemetry import QueryCondition, Span, SpanWithStatus, Tra class TraceStore(Protocol): async def query_traces( self, - attribute_filters: Optional[List[QueryCondition]] = None, - limit: Optional[int] = 100, - offset: Optional[int] = 0, - order_by: Optional[List[str]] = None, - ) -> List[Trace]: ... + attribute_filters: list[QueryCondition] | None = None, + limit: int | None = 100, + offset: int | None = 0, + order_by: list[str] | None = None, + ) -> list[Trace]: ... async def get_span_tree( self, span_id: str, - attributes_to_return: Optional[List[str]] = None, - max_depth: Optional[int] = None, - ) -> Dict[str, SpanWithStatus]: ... + attributes_to_return: list[str] | None = None, + max_depth: int | None = None, + ) -> dict[str, SpanWithStatus]: ... class SQLiteTraceStore(TraceStore): @@ -36,11 +36,11 @@ class SQLiteTraceStore(TraceStore): async def query_traces( self, - attribute_filters: Optional[List[QueryCondition]] = None, - limit: Optional[int] = 100, - offset: Optional[int] = 0, - order_by: Optional[List[str]] = None, - ) -> List[Trace]: + attribute_filters: list[QueryCondition] | None = None, + limit: int | None = 100, + offset: int | None = 0, + order_by: list[str] | None = None, + ) -> list[Trace]: def build_where_clause() -> tuple[str, list]: if not attribute_filters: return "", [] @@ -112,9 +112,9 @@ class SQLiteTraceStore(TraceStore): async def get_span_tree( self, span_id: str, - attributes_to_return: Optional[List[str]] = None, - max_depth: Optional[int] = None, - ) -> Dict[str, SpanWithStatus]: + attributes_to_return: list[str] | None = None, + max_depth: int | None = None, + ) -> dict[str, SpanWithStatus]: # Build the attributes selection attributes_select = "s.attributes" if attributes_to_return: diff --git a/llama_stack/providers/utils/telemetry/trace_protocol.py b/llama_stack/providers/utils/telemetry/trace_protocol.py index 525ade74d..eb6d8b331 100644 --- a/llama_stack/providers/utils/telemetry/trace_protocol.py +++ b/llama_stack/providers/utils/telemetry/trace_protocol.py @@ -7,8 +7,9 @@ import asyncio import inspect import json +from collections.abc import AsyncGenerator, Callable from functools import wraps -from typing import Any, AsyncGenerator, Callable, Type, TypeVar +from typing import Any, TypeVar from pydantic import BaseModel @@ -25,13 +26,13 @@ def _prepare_for_json(value: Any) -> str: """Serialize a single value into JSON-compatible format.""" if value is None: return "" - elif isinstance(value, (str, int, float, bool)): + elif isinstance(value, str | int | float | bool): return value elif hasattr(value, "_name_"): return value._name_ elif isinstance(value, BaseModel): return json.loads(value.model_dump_json()) - elif isinstance(value, (list, tuple, set)): + elif isinstance(value, list | tuple | set): return [_prepare_for_json(item) for item in value] elif isinstance(value, dict): return {str(k): _prepare_for_json(v) for k, v in value.items()} @@ -43,7 +44,7 @@ def _prepare_for_json(value: Any) -> str: return str(value) -def trace_protocol(cls: Type[T]) -> Type[T]: +def trace_protocol(cls: type[T]) -> type[T]: """ A class decorator that automatically traces all methods in a protocol/base class and its inheriting classes. diff --git a/llama_stack/providers/utils/telemetry/tracing.py b/llama_stack/providers/utils/telemetry/tracing.py index 3d5c717d6..0f4fdd0d8 100644 --- a/llama_stack/providers/utils/telemetry/tracing.py +++ b/llama_stack/providers/utils/telemetry/tracing.py @@ -10,9 +10,10 @@ import logging import queue import random import threading +from collections.abc import Callable from datetime import datetime, timezone from functools import wraps -from typing import Any, Callable, Dict, List, Optional +from typing import Any from llama_stack.apis.telemetry import ( LogSeverity, @@ -106,13 +107,13 @@ class BackgroundLogger: class TraceContext: - spans: List[Span] = [] + spans: list[Span] = [] def __init__(self, logger: BackgroundLogger, trace_id: str): self.logger = logger self.trace_id = trace_id - def push_span(self, name: str, attributes: Dict[str, Any] = None) -> Span: + def push_span(self, name: str, attributes: dict[str, Any] = None) -> Span: current_span = self.get_current_span() span = Span( span_id=generate_span_id(), @@ -168,7 +169,7 @@ def setup_logger(api: Telemetry, level: int = logging.INFO): root_logger.addHandler(TelemetryHandler()) -async def start_trace(name: str, attributes: Dict[str, Any] = None) -> TraceContext: +async def start_trace(name: str, attributes: dict[str, Any] = None) -> TraceContext: global CURRENT_TRACE_CONTEXT, BACKGROUND_LOGGER if BACKGROUND_LOGGER is None: @@ -246,7 +247,7 @@ class TelemetryHandler(logging.Handler): class SpanContextManager: - def __init__(self, name: str, attributes: Dict[str, Any] = None): + def __init__(self, name: str, attributes: dict[str, Any] = None): self.name = name self.attributes = attributes self.span = None @@ -316,11 +317,11 @@ class SpanContextManager: return wrapper -def span(name: str, attributes: Dict[str, Any] = None): +def span(name: str, attributes: dict[str, Any] = None): return SpanContextManager(name, attributes) -def get_current_span() -> Optional[Span]: +def get_current_span() -> Span | None: global CURRENT_TRACE_CONTEXT if CURRENT_TRACE_CONTEXT is None: logger.debug("No trace context to get current span") diff --git a/llama_stack/schema_utils.py b/llama_stack/schema_utils.py index 8143f1224..694de333e 100644 --- a/llama_stack/schema_utils.py +++ b/llama_stack/schema_utils.py @@ -4,37 +4,38 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +from collections.abc import Callable from dataclasses import dataclass -from typing import Any, Callable, List, Optional, TypeVar +from typing import Any, TypeVar from .strong_typing.schema import json_schema_type, register_schema # noqa: F401 @dataclass class WebMethod: - route: Optional[str] = None + route: str | None = None public: bool = False - request_examples: Optional[List[Any]] = None - response_examples: Optional[List[Any]] = None - method: Optional[str] = None - raw_bytes_request_body: Optional[bool] = False + request_examples: list[Any] | None = None + response_examples: list[Any] | None = None + method: str | None = None + raw_bytes_request_body: bool | None = False # A descriptive name of the corresponding span created by tracing - descriptive_name: Optional[str] = None - experimental: Optional[bool] = False + descriptive_name: str | None = None + experimental: bool | None = False T = TypeVar("T", bound=Callable[..., Any]) def webmethod( - route: Optional[str] = None, - method: Optional[str] = None, - public: Optional[bool] = False, - request_examples: Optional[List[Any]] = None, - response_examples: Optional[List[Any]] = None, - raw_bytes_request_body: Optional[bool] = False, - descriptive_name: Optional[str] = None, - experimental: Optional[bool] = False, + route: str | None = None, + method: str | None = None, + public: bool | None = False, + request_examples: list[Any] | None = None, + response_examples: list[Any] | None = None, + raw_bytes_request_body: bool | None = False, + descriptive_name: str | None = None, + experimental: bool | None = False, ) -> Callable[[T], T]: """ Decorator that supplies additional metadata to an endpoint operation function. diff --git a/llama_stack/strong_typing/docstring.py b/llama_stack/strong_typing/docstring.py index b038d1024..497c9ea82 100644 --- a/llama_stack/strong_typing/docstring.py +++ b/llama_stack/strong_typing/docstring.py @@ -11,6 +11,7 @@ Type-safe data interchange for Python data classes. """ import builtins +import collections.abc import dataclasses import inspect import re @@ -171,6 +172,13 @@ class SupportsDoc(Protocol): __doc__: Optional[str] +def _maybe_unwrap_async_iterator(t): + origin_type = typing.get_origin(t) + if origin_type is collections.abc.AsyncIterator: + return typing.get_args(t)[0] + return t + + def parse_type(typ: SupportsDoc) -> Docstring: """ Parse the docstring of a type into its components. @@ -178,6 +186,8 @@ def parse_type(typ: SupportsDoc) -> Docstring: :param typ: The type whose documentation string to parse. :returns: Components of the documentation string. """ + # Use docstring from the iterator origin type for streaming apis + typ = _maybe_unwrap_async_iterator(typ) doc = get_docstring(typ) if doc is None: diff --git a/llama_stack/strong_typing/schema.py b/llama_stack/strong_typing/schema.py index 0f5121906..82baddc86 100644 --- a/llama_stack/strong_typing/schema.py +++ b/llama_stack/strong_typing/schema.py @@ -10,6 +10,7 @@ Type-safe data interchange for Python data classes. :see: https://github.com/hunyadi/strong_typing """ +import collections.abc import dataclasses import datetime import decimal @@ -478,6 +479,8 @@ class JsonSchemaGenerator: } return ret elif origin_type is Literal: + if len(typing.get_args(typ)) != 1: + raise ValueError(f"Literal type {typ} has {len(typing.get_args(typ))} arguments") (literal_value,) = typing.get_args(typ) # unpack value of literal type schema = self.type_to_schema(type(literal_value)) schema["const"] = literal_value @@ -485,6 +488,9 @@ class JsonSchemaGenerator: elif origin_type is type: (concrete_type,) = typing.get_args(typ) # unpack single tuple element return {"const": self.type_to_schema(concrete_type, force_expand=True)} + elif origin_type is collections.abc.AsyncIterator: + (concrete_type,) = typing.get_args(typ) + return self.type_to_schema(concrete_type) # dictionary of class attributes members = dict(inspect.getmembers(typ, lambda a: not inspect.isroutine(a))) diff --git a/llama_stack/templates/bedrock/bedrock.py b/llama_stack/templates/bedrock/bedrock.py index f82defb4b..bc3a9304f 100644 --- a/llama_stack/templates/bedrock/bedrock.py +++ b/llama_stack/templates/bedrock/bedrock.py @@ -29,7 +29,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -55,10 +54,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] return DistributionTemplate( diff --git a/llama_stack/templates/bedrock/build.yaml b/llama_stack/templates/bedrock/build.yaml index 6c07b0478..46d5b9c69 100644 --- a/llama_stack/templates/bedrock/build.yaml +++ b/llama_stack/templates/bedrock/build.yaml @@ -26,7 +26,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/bedrock/run.yaml b/llama_stack/templates/bedrock/run.yaml index fe21d4bef..30599a6c0 100644 --- a/llama_stack/templates/bedrock/run.yaml +++ b/llama_stack/templates/bedrock/run.yaml @@ -39,9 +39,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/bedrock/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/bedrock}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -87,9 +87,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -140,7 +137,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/cerebras/build.yaml b/llama_stack/templates/cerebras/build.yaml index ef6c43212..0498da1cd 100644 --- a/llama_stack/templates/cerebras/build.yaml +++ b/llama_stack/templates/cerebras/build.yaml @@ -27,6 +27,5 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime image_type: conda diff --git a/llama_stack/templates/cerebras/cerebras.py b/llama_stack/templates/cerebras/cerebras.py index c370fb7d0..d891502d8 100644 --- a/llama_stack/templates/cerebras/cerebras.py +++ b/llama_stack/templates/cerebras/cerebras.py @@ -34,7 +34,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", ], } @@ -77,10 +76,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] return DistributionTemplate( diff --git a/llama_stack/templates/cerebras/report.md b/llama_stack/templates/cerebras/report.md index 7c09474b1..f240e354b 100644 --- a/llama_stack/templates/cerebras/report.md +++ b/llama_stack/templates/cerebras/report.md @@ -41,4 +41,3 @@ |:-----|:-----|:-----|:-----| | /create_agent_turn | rag | test_rag_agent | ✅ | | /create_agent_turn | custom_tool | test_custom_tool | ❌ | -| /create_agent_turn | code_execution | test_code_interpreter_for_attachments | ✅ | diff --git a/llama_stack/templates/cerebras/run.yaml b/llama_stack/templates/cerebras/run.yaml index dc7ee4729..0731b1df9 100644 --- a/llama_stack/templates/cerebras/run.yaml +++ b/llama_stack/templates/cerebras/run.yaml @@ -79,9 +79,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/cerebras/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/trace_store.db tool_runtime: - provider_id: brave-search provider_type: remote::brave-search @@ -93,9 +93,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -138,7 +135,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/ci-tests/build.yaml b/llama_stack/templates/ci-tests/build.yaml index a5c615f2f..a4c5893c4 100644 --- a/llama_stack/templates/ci-tests/build.yaml +++ b/llama_stack/templates/ci-tests/build.yaml @@ -27,7 +27,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/ci-tests/ci_tests.py b/llama_stack/templates/ci-tests/ci_tests.py index f6e836918..afa8a23ce 100644 --- a/llama_stack/templates/ci-tests/ci_tests.py +++ b/llama_stack/templates/ci-tests/ci_tests.py @@ -40,7 +40,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -71,10 +70,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] available_models = { "fireworks": MODEL_ENTRIES, diff --git a/llama_stack/templates/ci-tests/run.yaml b/llama_stack/templates/ci-tests/run.yaml index 3c16dd5ea..d9ee5b3cf 100644 --- a/llama_stack/templates/ci-tests/run.yaml +++ b/llama_stack/templates/ci-tests/run.yaml @@ -42,9 +42,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/ci-tests/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ci-tests}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -90,9 +90,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -236,7 +233,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/dell/build.yaml b/llama_stack/templates/dell/build.yaml index 05b98d56f..f5beb6c2f 100644 --- a/llama_stack/templates/dell/build.yaml +++ b/llama_stack/templates/dell/build.yaml @@ -28,6 +28,5 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime image_type: conda diff --git a/llama_stack/templates/dell/dell.py b/llama_stack/templates/dell/dell.py index 52c5a5476..a7ec5f3b8 100644 --- a/llama_stack/templates/dell/dell.py +++ b/llama_stack/templates/dell/dell.py @@ -30,7 +30,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", ], } @@ -87,10 +86,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] return DistributionTemplate( diff --git a/llama_stack/templates/dell/run-with-safety.yaml b/llama_stack/templates/dell/run-with-safety.yaml index 802c56aad..24c515112 100644 --- a/llama_stack/templates/dell/run-with-safety.yaml +++ b/llama_stack/templates/dell/run-with-safety.yaml @@ -45,9 +45,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/dell/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -93,9 +93,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -127,7 +124,5 @@ tool_groups: provider_id: brave-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/dell/run.yaml b/llama_stack/templates/dell/run.yaml index 4a2d819a9..fdece894f 100644 --- a/llama_stack/templates/dell/run.yaml +++ b/llama_stack/templates/dell/run.yaml @@ -41,9 +41,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/dell/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -89,9 +89,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -118,7 +115,5 @@ tool_groups: provider_id: brave-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/dependencies.json b/llama_stack/templates/dependencies.json index f8edb46da..31f2b93f1 100644 --- a/llama_stack/templates/dependencies.json +++ b/llama_stack/templates/dependencies.json @@ -344,6 +344,45 @@ "sentence-transformers --no-deps", "torch torchvision --index-url https://download.pytorch.org/whl/cpu" ], + "llama_api": [ + "aiosqlite", + "autoevals", + "blobfile", + "chardet", + "chromadb-client", + "datasets", + "emoji", + "fastapi", + "fire", + "httpx", + "langdetect", + "litellm", + "matplotlib", + "mcp", + "nltk", + "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", + "pandas", + "pillow", + "psycopg2-binary", + "pymongo", + "pypdf", + "pythainlp", + "redis", + "requests", + "scikit-learn", + "scipy", + "sentencepiece", + "sqlite-vec", + "tqdm", + "transformers", + "tree_sitter", + "uvicorn", + "sentence-transformers --no-deps", + "torch torchvision --index-url https://download.pytorch.org/whl/cpu" + ], "meta-reference-gpu": [ "accelerate", "aiosqlite", @@ -394,12 +433,11 @@ "aiosqlite", "blobfile", "chardet", - "emoji", + "datasets", "faiss-cpu", "fastapi", "fire", "httpx", - "langdetect", "matplotlib", "nltk", "numpy", @@ -411,7 +449,6 @@ "psycopg2-binary", "pymongo", "pypdf", - "pythainlp", "redis", "requests", "scikit-learn", @@ -419,7 +456,6 @@ "sentencepiece", "tqdm", "transformers", - "tree_sitter", "uvicorn" ], "ollama": [ @@ -762,5 +798,41 @@ "vllm", "sentence-transformers --no-deps", "torch torchvision --index-url https://download.pytorch.org/whl/cpu" + ], + "watsonx": [ + "aiosqlite", + "autoevals", + "blobfile", + "chardet", + "datasets", + "emoji", + "faiss-cpu", + "fastapi", + "fire", + "httpx", + "ibm_watson_machine_learning", + "langdetect", + "matplotlib", + "mcp", + "nltk", + "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", + "pandas", + "pillow", + "psycopg2-binary", + "pymongo", + "pypdf", + "pythainlp", + "redis", + "requests", + "scikit-learn", + "scipy", + "sentencepiece", + "tqdm", + "transformers", + "tree_sitter", + "uvicorn" ] } diff --git a/llama_stack/templates/dev/build.yaml b/llama_stack/templates/dev/build.yaml index f80892135..afa1614bf 100644 --- a/llama_stack/templates/dev/build.yaml +++ b/llama_stack/templates/dev/build.yaml @@ -32,7 +32,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/dev/dev.py b/llama_stack/templates/dev/dev.py index 260b6a53e..76d5a1fb3 100644 --- a/llama_stack/templates/dev/dev.py +++ b/llama_stack/templates/dev/dev.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List, Tuple from llama_stack.apis.models.models import ModelType from llama_stack.distribution.datatypes import ( @@ -54,7 +53,7 @@ from llama_stack.templates.template import ( ) -def get_inference_providers() -> Tuple[List[Provider], List[ModelInput]]: +def get_inference_providers() -> tuple[list[Provider], list[ModelInput]]: # in this template, we allow each API key to be optional providers = [ ( @@ -116,7 +115,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -159,10 +157,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] embedding_model = ModelInput( model_id="all-MiniLM-L6-v2", diff --git a/llama_stack/templates/dev/run.yaml b/llama_stack/templates/dev/run.yaml index 9dbd11b4a..b98498d53 100644 --- a/llama_stack/templates/dev/run.yaml +++ b/llama_stack/templates/dev/run.yaml @@ -76,9 +76,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/dev/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -124,9 +124,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -537,7 +534,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/fireworks/build.yaml b/llama_stack/templates/fireworks/build.yaml index 3907eba78..7c74157ee 100644 --- a/llama_stack/templates/fireworks/build.yaml +++ b/llama_stack/templates/fireworks/build.yaml @@ -28,7 +28,6 @@ distribution_spec: - remote::brave-search - remote::tavily-search - remote::wolfram-alpha - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/fireworks/fireworks.py b/llama_stack/templates/fireworks/fireworks.py index 449f18bf7..da68475e2 100644 --- a/llama_stack/templates/fireworks/fireworks.py +++ b/llama_stack/templates/fireworks/fireworks.py @@ -40,7 +40,6 @@ def get_distribution_template() -> DistributionTemplate: "remote::brave-search", "remote::tavily-search", "remote::wolfram-alpha", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -90,10 +89,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] return DistributionTemplate( diff --git a/llama_stack/templates/fireworks/report.md b/llama_stack/templates/fireworks/report.md index 2c1ccc943..b520acf8e 100644 --- a/llama_stack/templates/fireworks/report.md +++ b/llama_stack/templates/fireworks/report.md @@ -43,4 +43,3 @@ |:-----|:-----|:-----|:-----|:-----| | inline::meta-reference | /create_agent_turn | rag | test_rag_agent | ✅ | | inline::meta-reference | /create_agent_turn | custom_tool | test_custom_tool | ✅ | -| inline::meta-reference | /create_agent_turn | code_execution | test_code_interpreter_for_attachments | ✅ | diff --git a/llama_stack/templates/fireworks/run-with-safety.yaml b/llama_stack/templates/fireworks/run-with-safety.yaml index aa6209db6..0ab07613e 100644 --- a/llama_stack/templates/fireworks/run-with-safety.yaml +++ b/llama_stack/templates/fireworks/run-with-safety.yaml @@ -50,9 +50,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/fireworks/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -102,9 +102,6 @@ providers: provider_type: remote::wolfram-alpha config: api_key: ${env.WOLFRAM_ALPHA_API_KEY:} - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -255,7 +252,5 @@ tool_groups: provider_id: wolfram-alpha - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/fireworks/run.yaml b/llama_stack/templates/fireworks/run.yaml index 834ec8260..81c293a46 100644 --- a/llama_stack/templates/fireworks/run.yaml +++ b/llama_stack/templates/fireworks/run.yaml @@ -45,9 +45,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/fireworks/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -97,9 +97,6 @@ providers: provider_type: remote::wolfram-alpha config: api_key: ${env.WOLFRAM_ALPHA_API_KEY:} - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -245,7 +242,5 @@ tool_groups: provider_id: wolfram-alpha - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/groq/build.yaml b/llama_stack/templates/groq/build.yaml index 3263ce83b..800c3e3ae 100644 --- a/llama_stack/templates/groq/build.yaml +++ b/llama_stack/templates/groq/build.yaml @@ -24,6 +24,5 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime image_type: conda diff --git a/llama_stack/templates/groq/groq.py b/llama_stack/templates/groq/groq.py index 7999f95cb..4e52aa42d 100644 --- a/llama_stack/templates/groq/groq.py +++ b/llama_stack/templates/groq/groq.py @@ -33,7 +33,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", ], } @@ -72,10 +71,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] return DistributionTemplate( diff --git a/llama_stack/templates/groq/run.yaml b/llama_stack/templates/groq/run.yaml index 444452dcb..79c350c73 100644 --- a/llama_stack/templates/groq/run.yaml +++ b/llama_stack/templates/groq/run.yaml @@ -45,9 +45,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/groq/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/groq}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -93,9 +93,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -203,7 +200,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/hf-endpoint/build.yaml b/llama_stack/templates/hf-endpoint/build.yaml index c2eaaa05b..2a40c3909 100644 --- a/llama_stack/templates/hf-endpoint/build.yaml +++ b/llama_stack/templates/hf-endpoint/build.yaml @@ -26,7 +26,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/hf-endpoint/hf_endpoint.py b/llama_stack/templates/hf-endpoint/hf_endpoint.py index 53dc9d38f..69e037299 100644 --- a/llama_stack/templates/hf-endpoint/hf_endpoint.py +++ b/llama_stack/templates/hf-endpoint/hf_endpoint.py @@ -32,7 +32,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -79,10 +78,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] return DistributionTemplate( diff --git a/llama_stack/templates/hf-endpoint/run-with-safety.yaml b/llama_stack/templates/hf-endpoint/run-with-safety.yaml index 14753e08b..82bcaa3cf 100644 --- a/llama_stack/templates/hf-endpoint/run-with-safety.yaml +++ b/llama_stack/templates/hf-endpoint/run-with-safety.yaml @@ -50,9 +50,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/hf-endpoint/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -98,9 +98,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -135,7 +132,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/hf-endpoint/run.yaml b/llama_stack/templates/hf-endpoint/run.yaml index 706ba9122..ec7c55032 100644 --- a/llama_stack/templates/hf-endpoint/run.yaml +++ b/llama_stack/templates/hf-endpoint/run.yaml @@ -45,9 +45,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/hf-endpoint/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -93,9 +93,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -125,7 +122,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/hf-serverless/build.yaml b/llama_stack/templates/hf-serverless/build.yaml index c0cc1e2c2..f77f8773b 100644 --- a/llama_stack/templates/hf-serverless/build.yaml +++ b/llama_stack/templates/hf-serverless/build.yaml @@ -27,7 +27,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/hf-serverless/hf_serverless.py b/llama_stack/templates/hf-serverless/hf_serverless.py index ad8a72012..ecfe2a167 100644 --- a/llama_stack/templates/hf-serverless/hf_serverless.py +++ b/llama_stack/templates/hf-serverless/hf_serverless.py @@ -32,7 +32,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -80,10 +79,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] return DistributionTemplate( diff --git a/llama_stack/templates/hf-serverless/run-with-safety.yaml b/llama_stack/templates/hf-serverless/run-with-safety.yaml index bf26fe507..320976e2c 100644 --- a/llama_stack/templates/hf-serverless/run-with-safety.yaml +++ b/llama_stack/templates/hf-serverless/run-with-safety.yaml @@ -50,9 +50,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/hf-serverless/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -98,9 +98,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -135,7 +132,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/hf-serverless/run.yaml b/llama_stack/templates/hf-serverless/run.yaml index cc973b8de..2b22b20c6 100644 --- a/llama_stack/templates/hf-serverless/run.yaml +++ b/llama_stack/templates/hf-serverless/run.yaml @@ -45,9 +45,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/hf-serverless/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -93,9 +93,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -125,7 +122,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/llama_api/__init__.py b/llama_stack/templates/llama_api/__init__.py new file mode 100644 index 000000000..57cc75730 --- /dev/null +++ b/llama_stack/templates/llama_api/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from .llama_api import get_distribution_template # noqa: F401 diff --git a/llama_stack/templates/llama_api/build.yaml b/llama_stack/templates/llama_api/build.yaml new file mode 100644 index 000000000..f97ee4091 --- /dev/null +++ b/llama_stack/templates/llama_api/build.yaml @@ -0,0 +1,32 @@ +version: '2' +distribution_spec: + description: Distribution for running e2e tests in CI + providers: + inference: + - remote::llama-openai-compat + - inline::sentence-transformers + vector_io: + - inline::sqlite-vec + - remote::chromadb + - remote::pgvector + safety: + - inline::llama-guard + agents: + - inline::meta-reference + telemetry: + - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust + tool_runtime: + - remote::brave-search + - remote::tavily-search + - inline::rag-runtime + - remote::model-context-protocol +image_type: conda diff --git a/llama_stack/templates/llama_api/llama_api.py b/llama_stack/templates/llama_api/llama_api.py new file mode 100644 index 000000000..b4641b9da --- /dev/null +++ b/llama_stack/templates/llama_api/llama_api.py @@ -0,0 +1,153 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + + +from llama_stack.apis.models.models import ModelType +from llama_stack.distribution.datatypes import ( + ModelInput, + Provider, + ShieldInput, + ToolGroupInput, +) +from llama_stack.providers.inline.inference.sentence_transformers import ( + SentenceTransformersInferenceConfig, +) +from llama_stack.providers.inline.vector_io.sqlite_vec.config import ( + SQLiteVectorIOConfig, +) +from llama_stack.providers.remote.inference.llama_openai_compat.config import ( + LlamaCompatConfig, +) +from llama_stack.providers.remote.inference.llama_openai_compat.models import ( + MODEL_ENTRIES as LLLAMA_MODEL_ENTRIES, +) +from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig +from llama_stack.providers.remote.vector_io.pgvector.config import ( + PGVectorVectorIOConfig, +) +from llama_stack.templates.template import ( + DistributionTemplate, + RunConfigSettings, + get_model_registry, +) + + +def get_inference_providers() -> tuple[list[Provider], list[ModelInput]]: + # in this template, we allow each API key to be optional + providers = [ + ( + "llama-openai-compat", + LLLAMA_MODEL_ENTRIES, + LlamaCompatConfig.sample_run_config(api_key="${env.LLAMA_API_KEY:}"), + ), + ] + inference_providers = [] + available_models = {} + for provider_id, model_entries, config in providers: + inference_providers.append( + Provider( + provider_id=provider_id, + provider_type=f"remote::{provider_id}", + config=config, + ) + ) + available_models[provider_id] = model_entries + return inference_providers, available_models + + +def get_distribution_template() -> DistributionTemplate: + inference_providers, available_models = get_inference_providers() + providers = { + "inference": ([p.provider_type for p in inference_providers] + ["inline::sentence-transformers"]), + "vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"], + "safety": ["inline::llama-guard"], + "agents": ["inline::meta-reference"], + "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], + "tool_runtime": [ + "remote::brave-search", + "remote::tavily-search", + "inline::rag-runtime", + "remote::model-context-protocol", + ], + } + name = "llama_api" + + vector_io_providers = [ + Provider( + provider_id="sqlite-vec", + provider_type="inline::sqlite-vec", + config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"), + ), + Provider( + provider_id="${env.ENABLE_CHROMADB+chromadb}", + provider_type="remote::chromadb", + config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:}"), + ), + Provider( + provider_id="${env.ENABLE_PGVECTOR+pgvector}", + provider_type="remote::pgvector", + config=PGVectorVectorIOConfig.sample_run_config( + db="${env.PGVECTOR_DB:}", + user="${env.PGVECTOR_USER:}", + password="${env.PGVECTOR_PASSWORD:}", + ), + ), + ] + embedding_provider = Provider( + provider_id="sentence-transformers", + provider_type="inline::sentence-transformers", + config=SentenceTransformersInferenceConfig.sample_run_config(), + ) + + default_tool_groups = [ + ToolGroupInput( + toolgroup_id="builtin::websearch", + provider_id="tavily-search", + ), + ToolGroupInput( + toolgroup_id="builtin::rag", + provider_id="rag-runtime", + ), + ] + embedding_model = ModelInput( + model_id="all-MiniLM-L6-v2", + provider_id=embedding_provider.provider_id, + model_type=ModelType.embedding, + metadata={ + "embedding_dimension": 384, + }, + ) + + default_models = get_model_registry(available_models) + return DistributionTemplate( + name=name, + distro_type="self_hosted", + description="Distribution for running e2e tests in CI", + container_image=None, + template_path=None, + providers=providers, + available_models_by_provider=available_models, + run_configs={ + "run.yaml": RunConfigSettings( + provider_overrides={ + "inference": inference_providers + [embedding_provider], + "vector_io": vector_io_providers, + }, + default_models=default_models + [embedding_model], + default_tool_groups=default_tool_groups, + default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")], + ), + }, + run_config_env_vars={ + "LLAMA_STACK_PORT": ( + "8321", + "Port for the Llama Stack distribution server", + ), + }, + ) diff --git a/llama_stack/templates/llama_api/run.yaml b/llama_stack/templates/llama_api/run.yaml new file mode 100644 index 000000000..a879482d7 --- /dev/null +++ b/llama_stack/templates/llama_api/run.yaml @@ -0,0 +1,162 @@ +version: '2' +image_name: llama_api +apis: +- agents +- datasetio +- eval +- inference +- safety +- scoring +- telemetry +- tool_runtime +- vector_io +providers: + inference: + - provider_id: llama-openai-compat + provider_type: remote::llama-openai-compat + config: + openai_compat_api_base: https://api.llama.com/compat/v1/ + api_key: ${env.LLAMA_API_KEY:} + - provider_id: sentence-transformers + provider_type: inline::sentence-transformers + config: {} + vector_io: + - provider_id: sqlite-vec + provider_type: inline::sqlite-vec + config: + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/sqlite_vec.db + - provider_id: ${env.ENABLE_CHROMADB+chromadb} + provider_type: remote::chromadb + config: + url: ${env.CHROMADB_URL:} + - provider_id: ${env.ENABLE_PGVECTOR+pgvector} + provider_type: remote::pgvector + config: + host: ${env.PGVECTOR_HOST:localhost} + port: ${env.PGVECTOR_PORT:5432} + db: ${env.PGVECTOR_DB:} + user: ${env.PGVECTOR_USER:} + password: ${env.PGVECTOR_PASSWORD:} + safety: + - provider_id: llama-guard + provider_type: inline::llama-guard + config: + excluded_categories: [] + agents: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + persistence_store: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/agents_store.db + telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/trace_store.db + eval: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/meta_reference_eval.db + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/huggingface_datasetio.db + - provider_id: localfs + provider_type: inline::localfs + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/localfs_datasetio.db + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} + tool_runtime: + - provider_id: brave-search + provider_type: remote::brave-search + config: + api_key: ${env.BRAVE_SEARCH_API_KEY:} + max_results: 3 + - provider_id: tavily-search + provider_type: remote::tavily-search + config: + api_key: ${env.TAVILY_SEARCH_API_KEY:} + max_results: 3 + - provider_id: rag-runtime + provider_type: inline::rag-runtime + config: {} + - provider_id: model-context-protocol + provider_type: remote::model-context-protocol + config: {} +metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/registry.db +models: +- metadata: {} + model_id: Llama-3.3-70B-Instruct + provider_id: llama-openai-compat + provider_model_id: Llama-3.3-70B-Instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.3-70B-Instruct + provider_id: llama-openai-compat + provider_model_id: Llama-3.3-70B-Instruct + model_type: llm +- metadata: {} + model_id: Llama-4-Scout-17B-16E-Instruct-FP8 + provider_id: llama-openai-compat + provider_model_id: Llama-4-Scout-17B-16E-Instruct-FP8 + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-4-Scout-17B-16E-Instruct + provider_id: llama-openai-compat + provider_model_id: Llama-4-Scout-17B-16E-Instruct-FP8 + model_type: llm +- metadata: {} + model_id: Llama-4-Maverick-17B-128E-Instruct-FP8 + provider_id: llama-openai-compat + provider_model_id: Llama-4-Maverick-17B-128E-Instruct-FP8 + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-4-Maverick-17B-128E-Instruct + provider_id: llama-openai-compat + provider_model_id: Llama-4-Maverick-17B-128E-Instruct-FP8 + model_type: llm +- metadata: + embedding_dimension: 384 + model_id: all-MiniLM-L6-v2 + provider_id: sentence-transformers + model_type: embedding +shields: +- shield_id: meta-llama/Llama-Guard-3-8B +vector_dbs: [] +datasets: [] +scoring_fns: [] +benchmarks: [] +tool_groups: +- toolgroup_id: builtin::websearch + provider_id: tavily-search +- toolgroup_id: builtin::rag + provider_id: rag-runtime +server: + port: 8321 diff --git a/llama_stack/templates/meta-reference-gpu/build.yaml b/llama_stack/templates/meta-reference-gpu/build.yaml index b9130fc7d..a9d03490b 100644 --- a/llama_stack/templates/meta-reference-gpu/build.yaml +++ b/llama_stack/templates/meta-reference-gpu/build.yaml @@ -26,7 +26,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/meta-reference-gpu/doc_template.md b/llama_stack/templates/meta-reference-gpu/doc_template.md index a174331b4..2ca6793d7 100644 --- a/llama_stack/templates/meta-reference-gpu/doc_template.md +++ b/llama_stack/templates/meta-reference-gpu/doc_template.md @@ -69,6 +69,7 @@ LLAMA_STACK_PORT=8321 docker run \ -it \ --pull always \ + --gpu all \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ~/.llama:/root/.llama \ llamastack/distribution-{{ name }} \ @@ -82,6 +83,7 @@ If you are using Llama Stack Safety / Shield APIs, use: docker run \ -it \ --pull always \ + --gpu all \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ~/.llama:/root/.llama \ llamastack/distribution-{{ name }} \ diff --git a/llama_stack/templates/meta-reference-gpu/meta_reference.py b/llama_stack/templates/meta-reference-gpu/meta_reference.py index 8ba9fadca..95d126095 100644 --- a/llama_stack/templates/meta-reference-gpu/meta_reference.py +++ b/llama_stack/templates/meta-reference-gpu/meta_reference.py @@ -36,7 +36,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -86,10 +85,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] return DistributionTemplate( diff --git a/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml b/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml index 63177ab09..180d44e0f 100644 --- a/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml +++ b/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml @@ -60,9 +60,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/meta-reference-gpu/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -108,9 +108,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -145,7 +142,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/meta-reference-gpu/run.yaml b/llama_stack/templates/meta-reference-gpu/run.yaml index 380d83060..d879667e0 100644 --- a/llama_stack/templates/meta-reference-gpu/run.yaml +++ b/llama_stack/templates/meta-reference-gpu/run.yaml @@ -50,9 +50,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/meta-reference-gpu/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -98,9 +98,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -130,7 +127,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/nvidia/build.yaml b/llama_stack/templates/nvidia/build.yaml index f99ff6c81..a05cf97ad 100644 --- a/llama_stack/templates/nvidia/build.yaml +++ b/llama_stack/templates/nvidia/build.yaml @@ -1,6 +1,6 @@ version: '2' distribution_spec: - description: Use NVIDIA NIM for running LLM inference and safety + description: Use NVIDIA NIM for running LLM inference, evaluation and safety providers: inference: - remote::nvidia @@ -13,11 +13,12 @@ distribution_spec: telemetry: - inline::meta-reference eval: - - inline::meta-reference + - remote::nvidia post_training: - remote::nvidia datasetio: - inline::localfs + - remote::nvidia scoring: - inline::basic tool_runtime: diff --git a/llama_stack/templates/nvidia/doc_template.md b/llama_stack/templates/nvidia/doc_template.md index da95227d8..068dd7ac3 100644 --- a/llama_stack/templates/nvidia/doc_template.md +++ b/llama_stack/templates/nvidia/doc_template.md @@ -25,14 +25,84 @@ The following models are available by default: {% endif %} -### Prerequisite: API Keys +## Prerequisites +### NVIDIA API Keys -Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). +Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). Use this key for the `NVIDIA_API_KEY` environment variable. +### Deploy NeMo Microservices Platform +The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for platform prerequisites and instructions to install and deploy the platform. + +## Supported Services +Each Llama Stack API corresponds to a specific NeMo microservice. The core microservices (Customizer, Evaluator, Guardrails) are exposed by the same endpoint. The platform components (Data Store) are each exposed by separate endpoints. + +### Inference: NVIDIA NIM +NVIDIA NIM is used for running inference with registered models. There are two ways to access NVIDIA NIMs: + 1. Hosted (default): Preview APIs hosted at https://integrate.api.nvidia.com (Requires an API key) + 2. Self-hosted: NVIDIA NIMs that run on your own infrastructure. + +The deployed platform includes the NIM Proxy microservice, which is the service that provides to access your NIMs (for example, to run inference on a model). Set the `NVIDIA_BASE_URL` environment variable to use your NVIDIA NIM Proxy deployment. + +### Datasetio API: NeMo Data Store +The NeMo Data Store microservice serves as the default file storage solution for the NeMo microservices platform. It exposts APIs compatible with the Hugging Face Hub client (`HfApi`), so you can use the client to interact with Data Store. The `NVIDIA_DATASETS_URL` environment variable should point to your NeMo Data Store endpoint. + +See the [NVIDIA Datasetio docs](/llama_stack/providers/remote/datasetio/nvidia/README.md) for supported features and example usage. + +### Eval API: NeMo Evaluator +The NeMo Evaluator microservice supports evaluation of LLMs. Launching an Evaluation job with NeMo Evaluator requires an Evaluation Config (an object that contains metadata needed by the job). A Llama Stack Benchmark maps to an Evaluation Config, so registering a Benchmark creates an Evaluation Config in NeMo Evaluator. The `NVIDIA_EVALUATOR_URL` environment variable should point to your NeMo Microservices endpoint. + +See the [NVIDIA Eval docs](/llama_stack/providers/remote/eval/nvidia/README.md) for supported features and example usage. + +### Post-Training API: NeMo Customizer +The NeMo Customizer microservice supports fine-tuning models. You can reference [this list of supported models](/llama_stack/providers/remote/post_training/nvidia/models.py) that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint. + +See the [NVIDIA Post-Training docs](/llama_stack/providers/remote/post_training/nvidia/README.md) for supported features and example usage. + +### Safety API: NeMo Guardrails +The NeMo Guardrails microservice sits between your application and the LLM, and adds checks and content moderation to a model. The `GUARDRAILS_SERVICE_URL` environment variable should point to your NeMo Microservices endpoint. + +See the NVIDIA Safety docs for supported features and example usage. + +## Deploying models +In order to use a registered model with the Llama Stack APIs, ensure the corresponding NIM is deployed to your environment. For example, you can use the NIM Proxy microservice to deploy `meta/llama-3.2-1b-instruct`. + +Note: For improved inference speeds, we need to use NIM with `fast_outlines` guided decoding system (specified in the request body). This is the default if you deployed the platform with the NeMo Microservices Helm Chart. +```sh +# URL to NeMo NIM Proxy service +export NEMO_URL="http://nemo.test" + +curl --location "$NEMO_URL/v1/deployment/model-deployments" \ + -H 'accept: application/json' \ + -H 'Content-Type: application/json' \ + -d '{ + "name": "llama-3.2-1b-instruct", + "namespace": "meta", + "config": { + "model": "meta/llama-3.2-1b-instruct", + "nim_deployment": { + "image_name": "nvcr.io/nim/meta/llama-3.2-1b-instruct", + "image_tag": "1.8.3", + "pvc_size": "25Gi", + "gpu": 1, + "additional_envs": { + "NIM_GUIDED_DECODING_BACKEND": "fast_outlines" + } + } + } + }' +``` +This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/latest/get-started/tutorials/deploy-nims.html) for more information on how to deploy a NIM and verify it's available for inference. + +You can also remove a deployed NIM to free up GPU resources, if needed. +```sh +export NEMO_URL="http://nemo.test" + +curl -X DELETE "$NEMO_URL/v1/deployment/model-deployments/meta/llama-3.1-8b-instruct" +``` ## Running Llama Stack with NVIDIA -You can do this via Conda (build code) or Docker which has a pre-built image. +You can do this via Conda or venv (build code), or Docker which has a pre-built image. ### Via Docker @@ -54,9 +124,23 @@ docker run \ ### Via Conda ```bash +INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct llama stack build --template nvidia --image-type conda llama stack run ./run.yaml \ --port 8321 \ - --env NVIDIA_API_KEY=$NVIDIA_API_KEY + --env NVIDIA_API_KEY=$NVIDIA_API_KEY \ + --env INFERENCE_MODEL=$INFERENCE_MODEL +``` + +### Via venv + +If you've set up your local development environment, you can also build the image using your local virtual environment. + +```bash +INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct +llama stack build --template nvidia --image-type venv +llama stack run ./run.yaml \ + --port 8321 \ + --env NVIDIA_API_KEY=$NVIDIA_API_KEY \ --env INFERENCE_MODEL=$INFERENCE_MODEL ``` diff --git a/llama_stack/templates/nvidia/nvidia.py b/llama_stack/templates/nvidia/nvidia.py index 3b0cbe1e5..bfd004037 100644 --- a/llama_stack/templates/nvidia/nvidia.py +++ b/llama_stack/templates/nvidia/nvidia.py @@ -7,6 +7,8 @@ from pathlib import Path from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput, ToolGroupInput +from llama_stack.providers.remote.datasetio.nvidia import NvidiaDatasetIOConfig +from llama_stack.providers.remote.eval.nvidia import NVIDIAEvalConfig from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig from llama_stack.providers.remote.inference.nvidia.models import MODEL_ENTRIES from llama_stack.providers.remote.safety.nvidia import NVIDIASafetyConfig @@ -20,9 +22,9 @@ def get_distribution_template() -> DistributionTemplate: "safety": ["remote::nvidia"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], - "eval": ["inline::meta-reference"], + "eval": ["remote::nvidia"], "post_training": ["remote::nvidia"], - "datasetio": ["inline::localfs"], + "datasetio": ["inline::localfs", "remote::nvidia"], "scoring": ["inline::basic"], "tool_runtime": ["inline::rag-runtime"], } @@ -37,6 +39,16 @@ def get_distribution_template() -> DistributionTemplate: provider_type="remote::nvidia", config=NVIDIASafetyConfig.sample_run_config(), ) + datasetio_provider = Provider( + provider_id="nvidia", + provider_type="remote::nvidia", + config=NvidiaDatasetIOConfig.sample_run_config(), + ) + eval_provider = Provider( + provider_id="nvidia", + provider_type="remote::nvidia", + config=NVIDIAEvalConfig.sample_run_config(), + ) inference_model = ModelInput( model_id="${env.INFERENCE_MODEL}", provider_id="nvidia", @@ -59,8 +71,8 @@ def get_distribution_template() -> DistributionTemplate: default_models = get_model_registry(available_models) return DistributionTemplate( name="nvidia", - distro_type="remote_hosted", - description="Use NVIDIA NIM for running LLM inference and safety", + distro_type="self_hosted", + description="Use NVIDIA NIM for running LLM inference, evaluation and safety", container_image=None, template_path=Path(__file__).parent / "doc_template.md", providers=providers, @@ -69,6 +81,8 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], + "datasetio": [datasetio_provider], + "eval": [eval_provider], }, default_models=default_models, default_tool_groups=default_tool_groups, @@ -78,7 +92,8 @@ def get_distribution_template() -> DistributionTemplate: "inference": [ inference_provider, safety_provider, - ] + ], + "eval": [eval_provider], }, default_models=[inference_model, safety_model], default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}", provider_id="nvidia")], @@ -90,19 +105,15 @@ def get_distribution_template() -> DistributionTemplate: "", "NVIDIA API Key", ), - ## Nemo Customizer related variables - "NVIDIA_USER_ID": ( - "llama-stack-user", - "NVIDIA User ID", + "NVIDIA_APPEND_API_VERSION": ( + "True", + "Whether to append the API version to the base_url", ), + ## Nemo Customizer related variables "NVIDIA_DATASET_NAMESPACE": ( "default", "NVIDIA Dataset Namespace", ), - "NVIDIA_ACCESS_POLICIES": ( - "{}", - "NVIDIA Access Policies", - ), "NVIDIA_PROJECT_ID": ( "test-project", "NVIDIA Project ID", @@ -119,6 +130,10 @@ def get_distribution_template() -> DistributionTemplate: "http://0.0.0.0:7331", "URL for the NeMo Guardrails Service", ), + "NVIDIA_EVALUATOR_URL": ( + "http://0.0.0.0:7331", + "URL for the NeMo Evaluator Service", + ), "INFERENCE_MODEL": ( "Llama3.1-8B-Instruct", "Inference model", diff --git a/llama_stack/templates/nvidia/run-with-safety.yaml b/llama_stack/templates/nvidia/run-with-safety.yaml index 658d9377e..3cdb8e3d2 100644 --- a/llama_stack/templates/nvidia/run-with-safety.yaml +++ b/llama_stack/templates/nvidia/run-with-safety.yaml @@ -18,6 +18,7 @@ providers: config: url: ${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com} api_key: ${env.NVIDIA_API_KEY:} + append_api_version: ${env.NVIDIA_APPEND_API_VERSION:True} - provider_id: nvidia provider_type: remote::nvidia config: @@ -49,17 +50,14 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/nvidia/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/trace_store.db eval: - - provider_id: meta-reference - provider_type: inline::meta-reference + - provider_id: nvidia + provider_type: remote::nvidia config: - kvstore: - type: sqlite - namespace: null - db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/meta_reference_eval.db + evaluator_url: ${env.NVIDIA_EVALUATOR_URL:http://localhost:7331} post_training: - provider_id: nvidia provider_type: remote::nvidia @@ -76,6 +74,13 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/localfs_datasetio.db + - provider_id: nvidia + provider_type: remote::nvidia + config: + api_key: ${env.NVIDIA_API_KEY:} + dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:default} + project_id: ${env.NVIDIA_PROJECT_ID:test-project} + datasets_url: ${env.NVIDIA_DATASETS_URL:http://nemo.test} scoring: - provider_id: basic provider_type: inline::basic diff --git a/llama_stack/templates/nvidia/run.yaml b/llama_stack/templates/nvidia/run.yaml index 1267a9883..3337b7942 100644 --- a/llama_stack/templates/nvidia/run.yaml +++ b/llama_stack/templates/nvidia/run.yaml @@ -18,6 +18,7 @@ providers: config: url: ${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com} api_key: ${env.NVIDIA_API_KEY:} + append_api_version: ${env.NVIDIA_APPEND_API_VERSION:True} vector_io: - provider_id: faiss provider_type: inline::faiss @@ -44,17 +45,14 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/nvidia/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/trace_store.db eval: - - provider_id: meta-reference - provider_type: inline::meta-reference + - provider_id: nvidia + provider_type: remote::nvidia config: - kvstore: - type: sqlite - namespace: null - db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/meta_reference_eval.db + evaluator_url: ${env.NVIDIA_EVALUATOR_URL:http://localhost:7331} post_training: - provider_id: nvidia provider_type: remote::nvidia @@ -64,13 +62,13 @@ providers: project_id: ${env.NVIDIA_PROJECT_ID:test-project} customizer_url: ${env.NVIDIA_CUSTOMIZER_URL:http://nemo.test} datasetio: - - provider_id: localfs - provider_type: inline::localfs + - provider_id: nvidia + provider_type: remote::nvidia config: - kvstore: - type: sqlite - namespace: null - db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/localfs_datasetio.db + api_key: ${env.NVIDIA_API_KEY:} + dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:default} + project_id: ${env.NVIDIA_PROJECT_ID:test-project} + datasets_url: ${env.NVIDIA_DATASETS_URL:http://nemo.test} scoring: - provider_id: basic provider_type: inline::basic @@ -173,6 +171,16 @@ models: provider_id: nvidia provider_model_id: meta/llama-3.2-90b-vision-instruct model_type: llm +- metadata: {} + model_id: meta/llama-3.3-70b-instruct + provider_id: nvidia + provider_model_id: meta/llama-3.3-70b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.3-70B-Instruct + provider_id: nvidia + provider_model_id: meta/llama-3.3-70b-instruct + model_type: llm - metadata: embedding_dimension: 2048 context_length: 8192 diff --git a/llama_stack/templates/ollama/build.yaml b/llama_stack/templates/ollama/build.yaml index 37b72fc1f..88e61bf8a 100644 --- a/llama_stack/templates/ollama/build.yaml +++ b/llama_stack/templates/ollama/build.yaml @@ -26,7 +26,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol - remote::wolfram-alpha diff --git a/llama_stack/templates/ollama/ollama.py b/llama_stack/templates/ollama/ollama.py index d9f0960a2..d72d299ec 100644 --- a/llama_stack/templates/ollama/ollama.py +++ b/llama_stack/templates/ollama/ollama.py @@ -31,7 +31,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", "remote::wolfram-alpha", @@ -75,10 +74,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ToolGroupInput( toolgroup_id="builtin::wolfram_alpha", provider_id="wolfram-alpha", diff --git a/llama_stack/templates/ollama/report.md b/llama_stack/templates/ollama/report.md index 724809a59..4b2dada3a 100644 --- a/llama_stack/templates/ollama/report.md +++ b/llama_stack/templates/ollama/report.md @@ -41,4 +41,3 @@ |:-----|:-----|:-----|:-----| | /create_agent_turn | rag | test_rag_agent | ✅ | | /create_agent_turn | custom_tool | test_custom_tool | ✅ | -| /create_agent_turn | code_execution | test_code_interpreter_for_attachments | ✅ | diff --git a/llama_stack/templates/ollama/run-with-safety.yaml b/llama_stack/templates/ollama/run-with-safety.yaml index b43fec6db..651d58117 100644 --- a/llama_stack/templates/ollama/run-with-safety.yaml +++ b/llama_stack/templates/ollama/run-with-safety.yaml @@ -43,9 +43,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/ollama/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -91,9 +91,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -136,8 +133,6 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter - toolgroup_id: builtin::wolfram_alpha provider_id: wolfram-alpha server: diff --git a/llama_stack/templates/ollama/run.yaml b/llama_stack/templates/ollama/run.yaml index c8f4ad9ad..1372486fe 100644 --- a/llama_stack/templates/ollama/run.yaml +++ b/llama_stack/templates/ollama/run.yaml @@ -41,9 +41,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/ollama/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -89,9 +89,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -126,8 +123,6 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter - toolgroup_id: builtin::wolfram_alpha provider_id: wolfram-alpha server: diff --git a/llama_stack/templates/open-benchmark/build.yaml b/llama_stack/templates/open-benchmark/build.yaml index 1db90ef27..b14e96435 100644 --- a/llama_stack/templates/open-benchmark/build.yaml +++ b/llama_stack/templates/open-benchmark/build.yaml @@ -30,7 +30,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/open-benchmark/open_benchmark.py b/llama_stack/templates/open-benchmark/open_benchmark.py index a6a906c6f..d944d4eff 100644 --- a/llama_stack/templates/open-benchmark/open_benchmark.py +++ b/llama_stack/templates/open-benchmark/open_benchmark.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Dict, List, Tuple from llama_stack.apis.datasets import DatasetPurpose, URIDataSource from llama_stack.apis.models.models import ModelType @@ -36,7 +35,7 @@ from llama_stack.templates.template import ( ) -def get_inference_providers() -> Tuple[List[Provider], Dict[str, List[ProviderModelEntry]]]: +def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderModelEntry]]]: # in this template, we allow each API key to be optional providers = [ ( @@ -108,7 +107,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -146,10 +144,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] default_models = get_model_registry(available_models) + [ diff --git a/llama_stack/templates/open-benchmark/run.yaml b/llama_stack/templates/open-benchmark/run.yaml index 5e908b081..30a27cbd8 100644 --- a/llama_stack/templates/open-benchmark/run.yaml +++ b/llama_stack/templates/open-benchmark/run.yaml @@ -68,9 +68,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/open-benchmark/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open-benchmark}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -116,9 +116,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -242,7 +239,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/passthrough/build.yaml b/llama_stack/templates/passthrough/build.yaml index fb1fb1066..f8d099070 100644 --- a/llama_stack/templates/passthrough/build.yaml +++ b/llama_stack/templates/passthrough/build.yaml @@ -28,7 +28,6 @@ distribution_spec: - remote::brave-search - remote::tavily-search - remote::wolfram-alpha - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/passthrough/passthrough.py b/llama_stack/templates/passthrough/passthrough.py index 8454e49cf..6a30625c5 100644 --- a/llama_stack/templates/passthrough/passthrough.py +++ b/llama_stack/templates/passthrough/passthrough.py @@ -38,7 +38,6 @@ def get_distribution_template() -> DistributionTemplate: "remote::brave-search", "remote::tavily-search", "remote::wolfram-alpha", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -100,10 +99,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] return DistributionTemplate( diff --git a/llama_stack/templates/passthrough/run-with-safety.yaml b/llama_stack/templates/passthrough/run-with-safety.yaml index 8ab6b1081..a91b9fc92 100644 --- a/llama_stack/templates/passthrough/run-with-safety.yaml +++ b/llama_stack/templates/passthrough/run-with-safety.yaml @@ -50,9 +50,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/passthrough/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -102,9 +102,6 @@ providers: provider_type: remote::wolfram-alpha config: api_key: ${env.WOLFRAM_ALPHA_API_KEY:} - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -148,7 +145,5 @@ tool_groups: provider_id: wolfram-alpha - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/passthrough/run.yaml b/llama_stack/templates/passthrough/run.yaml index 53e8c8857..d1dd3b885 100644 --- a/llama_stack/templates/passthrough/run.yaml +++ b/llama_stack/templates/passthrough/run.yaml @@ -45,9 +45,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/passthrough/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -97,9 +97,6 @@ providers: provider_type: remote::wolfram-alpha config: api_key: ${env.WOLFRAM_ALPHA_API_KEY:} - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -138,7 +135,5 @@ tool_groups: provider_id: wolfram-alpha - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/remote-vllm/build.yaml b/llama_stack/templates/remote-vllm/build.yaml index b2bbf853a..4baaaf9c8 100644 --- a/llama_stack/templates/remote-vllm/build.yaml +++ b/llama_stack/templates/remote-vllm/build.yaml @@ -27,7 +27,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol - remote::wolfram-alpha diff --git a/llama_stack/templates/remote-vllm/doc_template.md b/llama_stack/templates/remote-vllm/doc_template.md index efcdb62c6..3cede6080 100644 --- a/llama_stack/templates/remote-vllm/doc_template.md +++ b/llama_stack/templates/remote-vllm/doc_template.md @@ -28,10 +28,10 @@ The following environment variables can be configured: ## Setting up vLLM server -In the following sections, we'll use either AMD and NVIDIA GPUs to serve as hardware accelerators for the vLLM +In the following sections, we'll use AMD, NVIDIA or Intel GPUs to serve as hardware accelerators for the vLLM server, which acts as both the LLM inference provider and the safety provider. Note that vLLM also [supports many other hardware accelerators](https://docs.vllm.ai/en/latest/getting_started/installation.html) and -that we only use GPUs here for demonstration purposes. +that we only use GPUs here for demonstration purposes. Note that if you run into issues, you can include the environment variable `--env VLLM_DEBUG_LOG_API_SERVER_RESPONSE=true` (available in vLLM v0.8.3 and above) in the `docker run` command to enable log response from API server for debugging. ### Setting up vLLM server on AMD GPU @@ -149,6 +149,55 @@ docker run \ --port $SAFETY_PORT ``` +### Setting up vLLM server on Intel GPU + +Refer to [vLLM Documentation for XPU](https://docs.vllm.ai/en/v0.8.2/getting_started/installation/gpu.html?device=xpu) to get a vLLM endpoint. In addition to vLLM side setup which guides towards installing vLLM from sources orself-building vLLM Docker container, Intel provides prebuilt vLLM container to use on systems with Intel GPUs supported by PyTorch XPU backend: +- [intel/vllm](https://hub.docker.com/r/intel/vllm) + +Here is a sample script to start a vLLM server locally via Docker using Intel provided container: + +```bash +export INFERENCE_PORT=8000 +export INFERENCE_MODEL=meta-llama/Llama-3.2-1B-Instruct +export ZE_AFFINITY_MASK=0 + +docker run \ + --pull always \ + --device /dev/dri \ + -v /dev/dri/by-path:/dev/dri/by-path \ + -v ~/.cache/huggingface:/root/.cache/huggingface \ + --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \ + --env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \ + -p $INFERENCE_PORT:$INFERENCE_PORT \ + --ipc=host \ + intel/vllm:xpu \ + --gpu-memory-utilization 0.7 \ + --model $INFERENCE_MODEL \ + --port $INFERENCE_PORT +``` + +If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like: + +```bash +export SAFETY_PORT=8081 +export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B +export ZE_AFFINITY_MASK=1 + +docker run \ + --pull always \ + --device /dev/dri \ + -v /dev/dri/by-path:/dev/dri/by-path \ + -v ~/.cache/huggingface:/root/.cache/huggingface \ + --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \ + --env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \ + -p $SAFETY_PORT:$SAFETY_PORT \ + --ipc=host \ + intel/vllm:xpu \ + --gpu-memory-utilization 0.7 \ + --model $SAFETY_MODEL \ + --port $SAFETY_PORT +``` + ## Running Llama Stack Now you are ready to run Llama Stack with vLLM as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image. diff --git a/llama_stack/templates/remote-vllm/run-with-safety.yaml b/llama_stack/templates/remote-vllm/run-with-safety.yaml index bb69496aa..6931d4ba9 100644 --- a/llama_stack/templates/remote-vllm/run-with-safety.yaml +++ b/llama_stack/templates/remote-vllm/run-with-safety.yaml @@ -88,9 +88,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/remote-vllm/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/trace_store.db tool_runtime: - provider_id: brave-search provider_type: remote::brave-search @@ -102,9 +102,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -143,8 +140,6 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter - toolgroup_id: builtin::wolfram_alpha provider_id: wolfram-alpha server: diff --git a/llama_stack/templates/remote-vllm/run.yaml b/llama_stack/templates/remote-vllm/run.yaml index 14f2da37e..05671165d 100644 --- a/llama_stack/templates/remote-vllm/run.yaml +++ b/llama_stack/templates/remote-vllm/run.yaml @@ -81,9 +81,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/remote-vllm/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/trace_store.db tool_runtime: - provider_id: brave-search provider_type: remote::brave-search @@ -95,9 +95,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -131,8 +128,6 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter - toolgroup_id: builtin::wolfram_alpha provider_id: wolfram-alpha server: diff --git a/llama_stack/templates/remote-vllm/vllm.py b/llama_stack/templates/remote-vllm/vllm.py index 0f6c7659e..2782a3ea0 100644 --- a/llama_stack/templates/remote-vllm/vllm.py +++ b/llama_stack/templates/remote-vllm/vllm.py @@ -34,7 +34,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", "remote::wolfram-alpha", @@ -84,10 +83,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ToolGroupInput( toolgroup_id="builtin::wolfram_alpha", provider_id="wolfram-alpha", diff --git a/llama_stack/templates/sambanova/build.yaml b/llama_stack/templates/sambanova/build.yaml index 9b09b9e90..81d90f420 100644 --- a/llama_stack/templates/sambanova/build.yaml +++ b/llama_stack/templates/sambanova/build.yaml @@ -18,7 +18,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol - remote::wolfram-alpha diff --git a/llama_stack/templates/sambanova/run.yaml b/llama_stack/templates/sambanova/run.yaml index 9a9164258..dcdb71ea1 100644 --- a/llama_stack/templates/sambanova/run.yaml +++ b/llama_stack/templates/sambanova/run.yaml @@ -54,9 +54,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/sambanova/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/trace_store.db tool_runtime: - provider_id: brave-search provider_type: remote::brave-search @@ -68,9 +68,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} diff --git a/llama_stack/templates/sambanova/sambanova.py b/llama_stack/templates/sambanova/sambanova.py index d48eebac1..7b54a825c 100644 --- a/llama_stack/templates/sambanova/sambanova.py +++ b/llama_stack/templates/sambanova/sambanova.py @@ -40,7 +40,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", "remote::wolfram-alpha", diff --git a/llama_stack/templates/template.py b/llama_stack/templates/template.py index 92b1b534d..e4d28d904 100644 --- a/llama_stack/templates/template.py +++ b/llama_stack/templates/template.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from pathlib import Path -from typing import Dict, List, Literal, Optional, Tuple +from typing import Literal import jinja2 import yaml @@ -32,8 +32,8 @@ from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig def get_model_registry( - available_models: Dict[str, List[ProviderModelEntry]], -) -> List[ModelInput]: + available_models: dict[str, list[ProviderModelEntry]], +) -> list[ModelInput]: models = [] for provider_id, entries in available_models.items(): for entry in entries: @@ -57,18 +57,18 @@ class DefaultModel(BaseModel): class RunConfigSettings(BaseModel): - provider_overrides: Dict[str, List[Provider]] = Field(default_factory=dict) - default_models: Optional[List[ModelInput]] = None - default_shields: Optional[List[ShieldInput]] = None - default_tool_groups: Optional[List[ToolGroupInput]] = None - default_datasets: Optional[List[DatasetInput]] = None - default_benchmarks: Optional[List[BenchmarkInput]] = None + provider_overrides: dict[str, list[Provider]] = Field(default_factory=dict) + default_models: list[ModelInput] | None = None + default_shields: list[ShieldInput] | None = None + default_tool_groups: list[ToolGroupInput] | None = None + default_datasets: list[DatasetInput] | None = None + default_benchmarks: list[BenchmarkInput] | None = None def run_config( self, name: str, - providers: Dict[str, List[str]], - container_image: Optional[str] = None, + providers: dict[str, list[str]], + container_image: str | None = None, ) -> StackRunConfig: provider_registry = get_provider_registry() @@ -135,15 +135,15 @@ class DistributionTemplate(BaseModel): description: str distro_type: Literal["self_hosted", "remote_hosted", "ondevice"] - providers: Dict[str, List[str]] - run_configs: Dict[str, RunConfigSettings] - template_path: Optional[Path] = None + providers: dict[str, list[str]] + run_configs: dict[str, RunConfigSettings] + template_path: Path | None = None # Optional configuration - run_config_env_vars: Optional[Dict[str, Tuple[str, str]]] = None - container_image: Optional[str] = None + run_config_env_vars: dict[str, tuple[str, str]] | None = None + container_image: str | None = None - available_models_by_provider: Optional[Dict[str, List[ProviderModelEntry]]] = None + available_models_by_provider: dict[str, list[ProviderModelEntry]] | None = None def build_config(self) -> BuildConfig: return BuildConfig( diff --git a/llama_stack/templates/tgi/build.yaml b/llama_stack/templates/tgi/build.yaml index 9fe79647c..d2ba1c3e9 100644 --- a/llama_stack/templates/tgi/build.yaml +++ b/llama_stack/templates/tgi/build.yaml @@ -27,7 +27,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/tgi/run-with-safety.yaml b/llama_stack/templates/tgi/run-with-safety.yaml index 12d6bd284..3255e9c0b 100644 --- a/llama_stack/templates/tgi/run-with-safety.yaml +++ b/llama_stack/templates/tgi/run-with-safety.yaml @@ -45,9 +45,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/tgi/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -93,9 +93,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -125,7 +122,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/tgi/run.yaml b/llama_stack/templates/tgi/run.yaml index 9f05c7584..179087258 100644 --- a/llama_stack/templates/tgi/run.yaml +++ b/llama_stack/templates/tgi/run.yaml @@ -44,9 +44,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/tgi/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -92,9 +92,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -124,7 +121,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/tgi/tgi.py b/llama_stack/templates/tgi/tgi.py index 22dcc3995..2c97cbf80 100644 --- a/llama_stack/templates/tgi/tgi.py +++ b/llama_stack/templates/tgi/tgi.py @@ -34,7 +34,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -83,10 +82,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] return DistributionTemplate( diff --git a/llama_stack/templates/together/build.yaml b/llama_stack/templates/together/build.yaml index 834a3ecaf..b7338795c 100644 --- a/llama_stack/templates/together/build.yaml +++ b/llama_stack/templates/together/build.yaml @@ -27,7 +27,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol - remote::wolfram-alpha diff --git a/llama_stack/templates/together/report.md b/llama_stack/templates/together/report.md index e125d5665..71ae83597 100644 --- a/llama_stack/templates/together/report.md +++ b/llama_stack/templates/together/report.md @@ -43,4 +43,3 @@ |:-----|:-----|:-----|:-----|:-----| | inline::meta-reference | /create_agent_turn | rag | test_rag_agent | ✅ | | inline::meta-reference | /create_agent_turn | custom_tool | test_custom_tool | ✅ | -| inline::meta-reference | /create_agent_turn | code_execution | test_code_interpreter_for_attachments | ✅ | diff --git a/llama_stack/templates/together/run-with-safety.yaml b/llama_stack/templates/together/run-with-safety.yaml index 105ce896d..fe8c8e397 100644 --- a/llama_stack/templates/together/run-with-safety.yaml +++ b/llama_stack/templates/together/run-with-safety.yaml @@ -50,9 +50,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/together/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -98,9 +98,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -270,8 +267,6 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter - toolgroup_id: builtin::wolfram_alpha provider_id: wolfram-alpha server: diff --git a/llama_stack/templates/together/run.yaml b/llama_stack/templates/together/run.yaml index 1f1613655..b903fc659 100644 --- a/llama_stack/templates/together/run.yaml +++ b/llama_stack/templates/together/run.yaml @@ -45,9 +45,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/together/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -93,9 +93,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -260,8 +257,6 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter - toolgroup_id: builtin::wolfram_alpha provider_id: wolfram-alpha server: diff --git a/llama_stack/templates/together/together.py b/llama_stack/templates/together/together.py index a2bd87c97..7761bd9fd 100644 --- a/llama_stack/templates/together/together.py +++ b/llama_stack/templates/together/together.py @@ -39,7 +39,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", "remote::wolfram-alpha", @@ -74,10 +73,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ToolGroupInput( toolgroup_id="builtin::wolfram_alpha", provider_id="wolfram-alpha", diff --git a/llama_stack/templates/verification/build.yaml b/llama_stack/templates/verification/build.yaml index 9f010d651..aae24c3ca 100644 --- a/llama_stack/templates/verification/build.yaml +++ b/llama_stack/templates/verification/build.yaml @@ -32,7 +32,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/verification/run.yaml b/llama_stack/templates/verification/run.yaml index f4bbf39f9..a9bf74330 100644 --- a/llama_stack/templates/verification/run.yaml +++ b/llama_stack/templates/verification/run.yaml @@ -78,9 +78,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/verification/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/verification}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -126,9 +126,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -640,7 +637,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/verification/verification.py b/llama_stack/templates/verification/verification.py index e6f74aad8..b58400f26 100644 --- a/llama_stack/templates/verification/verification.py +++ b/llama_stack/templates/verification/verification.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Dict, List, Tuple from llama_stack.apis.models.models import ModelType from llama_stack.distribution.datatypes import ( @@ -51,7 +50,7 @@ from llama_stack.templates.template import ( ) -def get_inference_providers() -> Tuple[List[Provider], Dict[str, List[ProviderModelEntry]]]: +def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderModelEntry]]]: # in this template, we allow each API key to be optional providers = [ ( @@ -113,7 +112,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -156,10 +154,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] embedding_model = ModelInput( model_id="all-MiniLM-L6-v2", diff --git a/llama_stack/templates/vllm-gpu/build.yaml b/llama_stack/templates/vllm-gpu/build.yaml index 8eb44dc1b..53e257f22 100644 --- a/llama_stack/templates/vllm-gpu/build.yaml +++ b/llama_stack/templates/vllm-gpu/build.yaml @@ -27,7 +27,6 @@ distribution_spec: tool_runtime: - remote::brave-search - remote::tavily-search - - inline::code-interpreter - inline::rag-runtime - remote::model-context-protocol image_type: conda diff --git a/llama_stack/templates/vllm-gpu/run.yaml b/llama_stack/templates/vllm-gpu/run.yaml index a839aa2c5..5d3482528 100644 --- a/llama_stack/templates/vllm-gpu/run.yaml +++ b/llama_stack/templates/vllm-gpu/run.yaml @@ -49,9 +49,9 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: "${env.OTEL_SERVICE_NAME:\u200B}" + service_name: ${env.OTEL_SERVICE_NAME:} sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/vllm-gpu/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/vllm-gpu}/trace_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -97,9 +97,6 @@ providers: config: api_key: ${env.TAVILY_SEARCH_API_KEY:} max_results: 3 - - provider_id: code-interpreter - provider_type: inline::code-interpreter - config: {} - provider_id: rag-runtime provider_type: inline::rag-runtime config: {} @@ -129,7 +126,5 @@ tool_groups: provider_id: tavily-search - toolgroup_id: builtin::rag provider_id: rag-runtime -- toolgroup_id: builtin::code_interpreter - provider_id: code-interpreter server: port: 8321 diff --git a/llama_stack/templates/vllm-gpu/vllm.py b/llama_stack/templates/vllm-gpu/vllm.py index 9bfeadc8d..5775138b1 100644 --- a/llama_stack/templates/vllm-gpu/vllm.py +++ b/llama_stack/templates/vllm-gpu/vllm.py @@ -31,7 +31,6 @@ def get_distribution_template() -> DistributionTemplate: "tool_runtime": [ "remote::brave-search", "remote::tavily-search", - "inline::code-interpreter", "inline::rag-runtime", "remote::model-context-protocol", ], @@ -75,10 +74,6 @@ def get_distribution_template() -> DistributionTemplate: toolgroup_id="builtin::rag", provider_id="rag-runtime", ), - ToolGroupInput( - toolgroup_id="builtin::code_interpreter", - provider_id="code-interpreter", - ), ] return DistributionTemplate( diff --git a/llama_stack/templates/watsonx/__init__.py b/llama_stack/templates/watsonx/__init__.py new file mode 100644 index 000000000..078d86144 --- /dev/null +++ b/llama_stack/templates/watsonx/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from .watsonx import get_distribution_template # noqa: F401 diff --git a/llama_stack/templates/watsonx/build.yaml b/llama_stack/templates/watsonx/build.yaml new file mode 100644 index 000000000..23a1ffa74 --- /dev/null +++ b/llama_stack/templates/watsonx/build.yaml @@ -0,0 +1,29 @@ +version: '2' +distribution_spec: + description: Use watsonx for running LLM inference + providers: + inference: + - remote::watsonx + vector_io: + - inline::faiss + safety: + - inline::llama-guard + agents: + - inline::meta-reference + telemetry: + - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust + tool_runtime: + - remote::brave-search + - remote::tavily-search + - inline::rag-runtime + - remote::model-context-protocol +image_type: conda diff --git a/llama_stack/templates/watsonx/doc_template.md b/llama_stack/templates/watsonx/doc_template.md new file mode 100644 index 000000000..af0ae15a8 --- /dev/null +++ b/llama_stack/templates/watsonx/doc_template.md @@ -0,0 +1,74 @@ +--- +orphan: true +--- +# watsonx Distribution + +```{toctree} +:maxdepth: 2 +:hidden: + +self +``` + +The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations. + +{{ providers_table }} + +{% if run_config_env_vars %} + +### Environment Variables + +The following environment variables can be configured: + +{% for var, (default_value, description) in run_config_env_vars.items() %} +- `{{ var }}`: {{ description }} (default: `{{ default_value }}`) +{% endfor %} +{% endif %} + +{% if default_models %} +### Models + +The following models are available by default: + +{% for model in default_models %} +- `{{ model.model_id }} {{ model.doc_string }}` +{% endfor %} +{% endif %} + + +### Prerequisite: API Keys + +Make sure you have access to a watsonx API Key. You can get one by referring [watsonx.ai](https://www.ibm.com/docs/en/masv-and-l/maximo-manage/continuous-delivery?topic=setup-create-watsonx-api-key). + + +## Running Llama Stack with watsonx + +You can do this via Conda (build code), venv or Docker which has a pre-built image. + +### Via Docker + +This method allows you to get started quickly without having to build the distribution code. + +```bash +LLAMA_STACK_PORT=5001 +docker run \ + -it \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ./run.yaml:/root/my-run.yaml \ + llamastack/distribution-{{ name }} \ + --yaml-config /root/my-run.yaml \ + --port $LLAMA_STACK_PORT \ + --env WATSONX_API_KEY=$WATSONX_API_KEY \ + --env WATSONX_PROJECT_ID=$WATSONX_PROJECT_ID \ + --env WATSONX_BASE_URL=$WATSONX_BASE_URL +``` + +### Via Conda + +```bash +llama stack build --template watsonx --image-type conda +llama stack run ./run.yaml \ + --port $LLAMA_STACK_PORT \ + --env WATSONX_API_KEY=$WATSONX_API_KEY \ + --env WATSONX_PROJECT_ID=$WATSONX_PROJECT_ID +``` diff --git a/llama_stack/templates/watsonx/run.yaml b/llama_stack/templates/watsonx/run.yaml new file mode 100644 index 000000000..dddb0670c --- /dev/null +++ b/llama_stack/templates/watsonx/run.yaml @@ -0,0 +1,205 @@ +version: '2' +image_name: watsonx +apis: +- agents +- datasetio +- eval +- inference +- safety +- scoring +- telemetry +- tool_runtime +- vector_io +providers: + inference: + - provider_id: watsonx + provider_type: remote::watsonx + config: + url: ${env.WATSONX_BASE_URL:https://us-south.ml.cloud.ibm.com} + api_key: ${env.WATSONX_API_KEY:} + project_id: ${env.WATSONX_PROJECT_ID:} + vector_io: + - provider_id: faiss + provider_type: inline::faiss + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/faiss_store.db + safety: + - provider_id: llama-guard + provider_type: inline::llama-guard + config: + excluded_categories: [] + agents: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + persistence_store: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/agents_store.db + telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/trace_store.db + eval: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/meta_reference_eval.db + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/huggingface_datasetio.db + - provider_id: localfs + provider_type: inline::localfs + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/localfs_datasetio.db + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} + tool_runtime: + - provider_id: brave-search + provider_type: remote::brave-search + config: + api_key: ${env.BRAVE_SEARCH_API_KEY:} + max_results: 3 + - provider_id: tavily-search + provider_type: remote::tavily-search + config: + api_key: ${env.TAVILY_SEARCH_API_KEY:} + max_results: 3 + - provider_id: rag-runtime + provider_type: inline::rag-runtime + config: {} + - provider_id: model-context-protocol + provider_type: remote::model-context-protocol + config: {} +metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/registry.db +models: +- metadata: {} + model_id: meta-llama/llama-3-3-70b-instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-3-70b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.3-70B-Instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-3-70b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/llama-2-13b-chat + provider_id: watsonx + provider_model_id: meta-llama/llama-2-13b-chat + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-2-13b + provider_id: watsonx + provider_model_id: meta-llama/llama-2-13b-chat + model_type: llm +- metadata: {} + model_id: meta-llama/llama-3-1-70b-instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-1-70b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.1-70B-Instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-1-70b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/llama-3-1-8b-instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-1-8b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.1-8B-Instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-1-8b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/llama-3-2-11b-vision-instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-2-11b-vision-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.2-11B-Vision-Instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-2-11b-vision-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/llama-3-2-1b-instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-2-1b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.2-1B-Instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-2-1b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/llama-3-2-3b-instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-2-3b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.2-3B-Instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-2-3b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/llama-3-2-90b-vision-instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-2-90b-vision-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.2-90B-Vision-Instruct + provider_id: watsonx + provider_model_id: meta-llama/llama-3-2-90b-vision-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/llama-guard-3-11b-vision + provider_id: watsonx + provider_model_id: meta-llama/llama-guard-3-11b-vision + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-Guard-3-11B-Vision + provider_id: watsonx + provider_model_id: meta-llama/llama-guard-3-11b-vision + model_type: llm +shields: [] +vector_dbs: [] +datasets: [] +scoring_fns: [] +benchmarks: [] +tool_groups: +- toolgroup_id: builtin::websearch + provider_id: tavily-search +- toolgroup_id: builtin::rag + provider_id: rag-runtime +server: + port: 8321 diff --git a/llama_stack/templates/watsonx/watsonx.py b/llama_stack/templates/watsonx/watsonx.py new file mode 100644 index 000000000..f16593051 --- /dev/null +++ b/llama_stack/templates/watsonx/watsonx.py @@ -0,0 +1,85 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from pathlib import Path + +from llama_stack.distribution.datatypes import Provider, ToolGroupInput +from llama_stack.providers.remote.inference.watsonx import WatsonXConfig +from llama_stack.providers.remote.inference.watsonx.models import MODEL_ENTRIES +from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry + + +def get_distribution_template() -> DistributionTemplate: + providers = { + "inference": ["remote::watsonx"], + "vector_io": ["inline::faiss"], + "safety": ["inline::llama-guard"], + "agents": ["inline::meta-reference"], + "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], + "tool_runtime": [ + "remote::brave-search", + "remote::tavily-search", + "inline::rag-runtime", + "remote::model-context-protocol", + ], + } + + inference_provider = Provider( + provider_id="watsonx", + provider_type="remote::watsonx", + config=WatsonXConfig.sample_run_config(), + ) + + available_models = { + "watsonx": MODEL_ENTRIES, + } + default_tool_groups = [ + ToolGroupInput( + toolgroup_id="builtin::websearch", + provider_id="tavily-search", + ), + ToolGroupInput( + toolgroup_id="builtin::rag", + provider_id="rag-runtime", + ), + ] + + default_models = get_model_registry(available_models) + return DistributionTemplate( + name="watsonx", + distro_type="remote_hosted", + description="Use watsonx for running LLM inference", + container_image=None, + template_path=Path(__file__).parent / "doc_template.md", + providers=providers, + available_models_by_provider=available_models, + run_configs={ + "run.yaml": RunConfigSettings( + provider_overrides={ + "inference": [inference_provider], + }, + default_models=default_models, + default_tool_groups=default_tool_groups, + ), + }, + run_config_env_vars={ + "LLAMASTACK_PORT": ( + "5001", + "Port for the Llama Stack distribution server", + ), + "WATSONX_API_KEY": ( + "", + "watsonx API Key", + ), + "WATSONX_PROJECT_ID": ( + "", + "watsonx Project ID", + ), + }, + ) diff --git a/pyproject.toml b/pyproject.toml index 7e910f673..192d82bd8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "llama_stack" -version = "0.2.1" +version = "0.2.5" authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }] description = "Llama Stack" readme = "README.md" @@ -27,7 +27,7 @@ dependencies = [ "huggingface-hub", "jinja2>=3.1.6", "jsonschema", - "llama-stack-client>=0.2.2", + "llama-stack-client>=0.2.5", "openai>=1.66", "prompt-toolkit", "python-dotenv", @@ -38,6 +38,8 @@ dependencies = [ "termcolor", "tiktoken", "pillow", + "h11>=0.16.0", + "kubernetes", ] [project.optional-dependencies] @@ -46,7 +48,8 @@ dev = [ "pytest-asyncio", "pytest-cov", "pytest-html", - "nbval", # For notebook testing + "pytest-json-report", + "nbval", # For notebook testing "black", "ruff", "types-requests", @@ -54,10 +57,19 @@ dev = [ "pre-commit", "uvicorn", "fastapi", - "ruamel.yaml", # needed for openapi generator + "ruamel.yaml", # needed for openapi generator ] # These are the dependencies required for running unit tests. -unit = ["sqlite-vec", "openai", "aiosqlite", "aiohttp", "pypdf", "chardet", "qdrant-client"] +unit = [ + "sqlite-vec", + "openai", + "aiosqlite", + "aiohttp", + "pypdf", + "chardet", + "qdrant-client", + "opentelemetry-exporter-otlp-proto-http", +] # These are the core dependencies required for running integration tests. They are shared across all # providers. If a provider requires additional dependencies, please add them to your environment # separately. If you are using "uv" to execute your tests, you can use the "--with" flag to specify extra @@ -93,7 +105,7 @@ codegen = ["rich", "pydantic", "jinja2>=3.1.6"] ui = [ "streamlit", "pandas", - "llama-stack-client>=0.2.1", + "llama-stack-client>=0.2.5", "streamlit-option-menu", ] @@ -132,15 +144,26 @@ exclude = [ [tool.ruff.lint] select = [ - "B", # flake8-bugbear - "B9", # flake8-bugbear subset - "C", # comprehensions - "E", # pycodestyle - "F", # Pyflakes - "N", # Naming - "W", # Warnings - "DTZ", # datetime rules - "I", # isort (imports order) + "UP", # pyupgrade + "B", # flake8-bugbear + "B9", # flake8-bugbear subset + "C", # comprehensions + "E", # pycodestyle + "F", # Pyflakes + "N", # Naming + "W", # Warnings + "DTZ", # datetime rules + "I", # isort (imports order) + "RUF001", # Checks for ambiguous Unicode characters in strings + "RUF002", # Checks for ambiguous Unicode characters in docstrings + "RUF003", # Checks for ambiguous Unicode characters in comments + "PLC2401", # Checks for the use of non-ASCII characters in variable names + "PLC2403", # Checks for the use of non-ASCII characters in import statements + "PLE2510", # Checks for strings that contain the control character BS. + "PLE2512", # Checks for strings that contain the raw control character SUB. + "PLE2513", # Checks for strings that contain the raw control character ESC. + "PLE2514", # Checks for strings that contain the raw control character NUL (0 byte). + "PLE2515", # Checks for strings that contain the zero width space character. ] ignore = [ # The following ignores are desired by the project maintainers. @@ -153,10 +176,18 @@ ignore = [ # These are the additional ones we started ignoring after moving to ruff. We should look into each one of them later. "C901", # Complexity of the function is too high ] +unfixable = [ + "PLE2515", +] # Do not fix this automatically since ruff will replace the zero-width space with \u200b - let's do it manually # Ignore the following errors for the following files [tool.ruff.lint.per-file-ignores] "tests/**/*.py" = ["DTZ"] # Ignore datetime rules for tests +"llama_stack/providers/inline/scoring/basic/utils/ifeval_utils.py" = ["RUF001"] +"llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/regex_parser_multiple_choice_answer.py" = [ + "RUF001", + "PLE2515", +] [tool.mypy] mypy_path = ["llama_stack"] @@ -249,6 +280,7 @@ exclude = [ "^llama_stack/providers/inline/scoring/llm_as_judge/", "^llama_stack/providers/remote/agents/sample/", "^llama_stack/providers/remote/datasetio/huggingface/", + "^llama_stack/providers/remote/datasetio/nvidia/", "^llama_stack/providers/remote/inference/anthropic/", "^llama_stack/providers/remote/inference/bedrock/", "^llama_stack/providers/remote/inference/cerebras/", @@ -264,6 +296,7 @@ exclude = [ "^llama_stack/providers/remote/inference/sample/", "^llama_stack/providers/remote/inference/tgi/", "^llama_stack/providers/remote/inference/together/", + "^llama_stack/providers/remote/inference/watsonx/", "^llama_stack/providers/remote/safety/bedrock/", "^llama_stack/providers/remote/safety/nvidia/", "^llama_stack/providers/remote/safety/sample/", @@ -307,6 +340,7 @@ exclude = [ "^llama_stack/strong_typing/serializer\\.py$", "^llama_stack/templates/dev/dev\\.py$", "^llama_stack/templates/groq/groq\\.py$", + "^llama_stack/templates/llama_api/llama_api\\.py$", "^llama_stack/templates/sambanova/sambanova\\.py$", "^llama_stack/templates/template\\.py$", ] diff --git a/requirements.txt b/requirements.txt index 2961b1533..194af774b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,17 +4,20 @@ annotated-types==0.7.0 anyio==4.8.0 attrs==25.1.0 blobfile==3.0.0 +cachetools==5.5.2 certifi==2025.1.31 charset-normalizer==3.4.1 click==8.1.8 colorama==0.4.6 ; sys_platform == 'win32' distro==1.9.0 +durationpy==0.9 exceptiongroup==1.2.2 ; python_full_version < '3.11' filelock==3.17.0 fire==0.7.0 fsspec==2024.12.0 -h11==0.14.0 -httpcore==1.0.7 +google-auth==2.38.0 +h11==0.16.0 +httpcore==1.0.9 httpx==0.28.1 huggingface-hub==0.29.0 idna==3.10 @@ -22,18 +25,22 @@ jinja2==3.1.6 jiter==0.8.2 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 -llama-stack-client==0.2.2 +kubernetes==32.0.1 +llama-stack-client==0.2.5 lxml==5.3.1 markdown-it-py==3.0.0 markupsafe==3.0.2 mdurl==0.1.2 numpy==2.2.3 +oauthlib==3.2.2 openai==1.71.0 packaging==24.2 pandas==2.2.3 pillow==11.1.0 prompt-toolkit==3.0.50 pyaml==25.1.0 +pyasn1==0.6.1 +pyasn1-modules==0.4.2 pycryptodomex==3.21.0 pydantic==2.10.6 pydantic-core==2.27.2 @@ -45,8 +52,10 @@ pyyaml==6.0.2 referencing==0.36.2 regex==2024.11.6 requests==2.32.3 +requests-oauthlib==2.0.0 rich==13.9.4 rpds-py==0.22.3 +rsa==4.9 setuptools==75.8.0 six==1.17.0 sniffio==1.3.1 @@ -57,3 +66,4 @@ typing-extensions==4.12.2 tzdata==2025.1 urllib3==2.3.0 wcwidth==0.2.13 +websocket-client==1.8.0 diff --git a/scripts/distro_codegen.py b/scripts/distro_codegen.py index 98faa53a3..30f533883 100755 --- a/scripts/distro_codegen.py +++ b/scripts/distro_codegen.py @@ -10,9 +10,9 @@ import importlib import json import subprocess import sys +from collections.abc import Iterable from functools import partial from pathlib import Path -from typing import Iterable from rich.progress import Progress, SpinnerColumn, TextColumn @@ -98,7 +98,7 @@ def collect_template_dependencies(template_dir: Path) -> tuple[str | None, list[ if template_func := getattr(module, "get_distribution_template", None): template = template_func() - normal_deps, special_deps = get_provider_dependencies(template.providers) + normal_deps, special_deps = get_provider_dependencies(template) # Combine all dependencies in order: normal deps, special deps, server deps all_deps = sorted(set(normal_deps + SERVER_DEPENDENCIES)) + sorted(set(special_deps)) diff --git a/tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml b/tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml new file mode 100644 index 000000000..eb3b85e52 --- /dev/null +++ b/tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml @@ -0,0 +1,9 @@ +version: '2' +distribution_spec: + description: Custom distro for CI tests + providers: + inference: + - remote::custom_ollama +image_type: container +image_name: ci-test +external_providers_dir: /tmp/providers.d diff --git a/tests/external-provider/llama-stack-provider-ollama/custom_ollama.yaml b/tests/external-provider/llama-stack-provider-ollama/custom_ollama.yaml index f0960b4d8..2ae1e2cf3 100644 --- a/tests/external-provider/llama-stack-provider-ollama/custom_ollama.yaml +++ b/tests/external-provider/llama-stack-provider-ollama/custom_ollama.yaml @@ -1,6 +1,6 @@ adapter: adapter_type: custom_ollama - pip_packages: ["ollama", "aiohttp"] + pip_packages: ["ollama", "aiohttp", "tests/external-provider/llama-stack-provider-ollama"] config_class: llama_stack_provider_ollama.config.OllamaImplConfig module: llama_stack_provider_ollama api_dependencies: [] diff --git a/tests/external-provider/llama-stack-provider-ollama/run.yaml b/tests/external-provider/llama-stack-provider-ollama/run.yaml index 7a3636c4d..5afeb1448 100644 --- a/tests/external-provider/llama-stack-provider-ollama/run.yaml +++ b/tests/external-provider/llama-stack-provider-ollama/run.yaml @@ -1,14 +1,10 @@ version: '2' image_name: ollama apis: -- agents -- datasetio -- eval - inference -- safety -- scoring - telemetry - tool_runtime +- datasetio - vector_io providers: inference: @@ -24,34 +20,13 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/faiss_store.db - safety: - - provider_id: llama-guard - provider_type: inline::llama-guard - config: - excluded_categories: [] - agents: - - provider_id: meta-reference - provider_type: inline::meta-reference - config: - persistence_store: - type: sqlite - namespace: null - db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/agents_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + service_name: "${env.OTEL_SERVICE_NAME:\u200B}" sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/ollama/trace_store.db} - eval: - - provider_id: meta-reference - provider_type: inline::meta-reference - config: - kvstore: - type: sqlite - namespace: null - db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/meta_reference_eval.db + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/trace_store.db datasetio: - provider_id: huggingface provider_type: remote::huggingface @@ -67,17 +42,6 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/localfs_datasetio.db - scoring: - - provider_id: basic - provider_type: inline::basic - config: {} - - provider_id: llm-as-judge - provider_type: inline::llm-as-judge - config: {} - - provider_id: braintrust - provider_type: inline::braintrust - config: - openai_api_key: ${env.OPENAI_API_KEY:} tool_runtime: - provider_id: brave-search provider_type: remote::brave-search diff --git a/tests/integration/agents/test_agents.py b/tests/integration/agents/test_agents.py index 7def55291..367fe1b9a 100644 --- a/tests/integration/agents/test_agents.py +++ b/tests/integration/agents/test_agents.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from uuid import uuid4 import pytest @@ -37,7 +37,7 @@ def get_boiling_point(liquid_name: str, celcius: bool = True) -> int: return -1 -def get_boiling_point_with_metadata(liquid_name: str, celcius: bool = True) -> Dict[str, Any]: +def get_boiling_point_with_metadata(liquid_name: str, celcius: bool = True) -> dict[str, Any]: """ Returns the boiling point of a liquid in Celcius or Fahrenheit @@ -115,6 +115,70 @@ def test_agent_simple(llama_stack_client_with_mocked_inference, agent_config): assert "I can't" in logs_str +def test_agent_name(llama_stack_client, text_model_id): + agent_name = f"test-agent-{uuid4()}" + + try: + agent = Agent( + llama_stack_client, + model=text_model_id, + instructions="You are a helpful assistant", + name=agent_name, + ) + except TypeError: + agent = Agent( + llama_stack_client, + model=text_model_id, + instructions="You are a helpful assistant", + ) + return + + session_id = agent.create_session(f"test-session-{uuid4()}") + + agent.create_turn( + messages=[ + { + "role": "user", + "content": "Give me a sentence that contains the word: hello", + } + ], + session_id=session_id, + stream=False, + ) + + all_spans = [] + for span in llama_stack_client.telemetry.query_spans( + attribute_filters=[ + {"key": "session_id", "op": "eq", "value": session_id}, + ], + attributes_to_return=["input", "output", "agent_name", "agent_id", "session_id"], + ): + all_spans.append(span.attributes) + + agent_name_spans = [] + for span in llama_stack_client.telemetry.query_spans( + attribute_filters=[], + attributes_to_return=["agent_name"], + ): + if "agent_name" in span.attributes: + agent_name_spans.append(span.attributes) + + agent_logs = [] + for span in llama_stack_client.telemetry.query_spans( + attribute_filters=[ + {"key": "agent_name", "op": "eq", "value": agent_name}, + ], + attributes_to_return=["input", "output", "agent_name"], + ): + if "output" in span.attributes and span.attributes["output"] != "no shields": + agent_logs.append(span.attributes) + + assert len(agent_logs) == 1 + assert agent_logs[0]["agent_name"] == agent_name + assert "Give me a sentence that contains the word: hello" in agent_logs[0]["input"] + assert "hello" in agent_logs[0]["output"].lower() + + def test_tool_config(llama_stack_client_with_mocked_inference, agent_config): common_params = dict( model="meta-llama/Llama-3.2-3B-Instruct", @@ -231,6 +295,7 @@ def test_builtin_tool_code_execution(llama_stack_client_with_mocked_inference, a # This test must be run in an environment where `bwrap` is available. If you are running against a # server, this means the _server_ must have `bwrap` available. If you are using library client, then # you must have `bwrap` available in test's environment. +@pytest.mark.skip(reason="Code interpreter is currently disabled in the Stack") def test_code_interpreter_for_attachments(llama_stack_client_with_mocked_inference, agent_config): agent_config = { **agent_config, @@ -487,6 +552,7 @@ def test_rag_agent_with_attachments(llama_stack_client_with_mocked_inference, ag assert "lora" in response.output_message.content.lower() +@pytest.mark.skip(reason="Code interpreter is currently disabled in the Stack") def test_rag_and_code_agent(llama_stack_client_with_mocked_inference, agent_config): if "llama-4" in agent_config["model"].lower(): pytest.xfail("Not working for llama4") diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index 22290b519..131219e52 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -10,6 +10,7 @@ import platform import textwrap import time +import pytest from dotenv import load_dotenv from llama_stack.log import get_logger @@ -19,10 +20,29 @@ from .report import Report logger = get_logger(__name__, category="tests") +@pytest.hookimpl(hookwrapper=True) +def pytest_runtest_makereport(item, call): + outcome = yield + report = outcome.get_result() + if report.when == "call": + item.execution_outcome = report.outcome + item.was_xfail = getattr(report, "wasxfail", False) + + def pytest_runtest_teardown(item): - interval_seconds = os.getenv("LLAMA_STACK_TEST_INTERVAL_SECONDS") - if interval_seconds: - time.sleep(float(interval_seconds)) + # Check if the test actually ran and passed or failed, but was not skipped or an expected failure (xfail) + outcome = getattr(item, "execution_outcome", None) + was_xfail = getattr(item, "was_xfail", False) + + name = item.nodeid + if not any(x in name for x in ("inference/", "safety/", "agents/")): + return + + logger.debug(f"Test '{item.nodeid}' outcome was '{outcome}' (xfail={was_xfail})") + if outcome in ("passed", "failed") and not was_xfail: + interval_seconds = os.getenv("LLAMA_STACK_TEST_INTERVAL_SECONDS") + if interval_seconds: + time.sleep(float(interval_seconds)) def pytest_configure(config): diff --git a/tests/integration/datasets/test_datasets.py b/tests/integration/datasets/test_datasets.py index 60db95f30..18b31d39c 100644 --- a/tests/integration/datasets/test_datasets.py +++ b/tests/integration/datasets/test_datasets.py @@ -31,6 +31,7 @@ def data_url_from_file(file_path: str) -> str: return data_url +@pytest.mark.skip(reason="flaky. Couldn't find 'llamastack/simpleqa' on the Hugging Face Hub") @pytest.mark.parametrize( "purpose, source, provider_id, limit", [ diff --git a/tests/integration/fixtures/common.py b/tests/integration/fixtures/common.py index 1878c9e88..809a00897 100644 --- a/tests/integration/fixtures/common.py +++ b/tests/integration/fixtures/common.py @@ -14,6 +14,7 @@ from pathlib import Path import pytest import yaml from llama_stack_client import LlamaStackClient +from openai import OpenAI from llama_stack import LlamaStackAsLibraryClient from llama_stack.apis.datatypes import Api @@ -207,3 +208,9 @@ def llama_stack_client(request, provider_data, text_model_id): raise RuntimeError("Initialization failed") return client + + +@pytest.fixture(scope="session") +def openai_client(client_with_models): + base_url = f"{client_with_models.base_url}/v1/openai/v1" + return OpenAI(base_url=base_url, api_key="fake") diff --git a/tests/integration/fixtures/recordable_mock.py b/tests/integration/fixtures/recordable_mock.py index 632d5b3ef..140831cfe 100644 --- a/tests/integration/fixtures/recordable_mock.py +++ b/tests/integration/fixtures/recordable_mock.py @@ -24,7 +24,7 @@ class RecordableMock: # Load existing cache if available and not recording if self.json_path.exists(): try: - with open(self.json_path, "r") as f: + with open(self.json_path) as f: self.cache = json.load(f) except Exception as e: print(f"Error loading cache from {self.json_path}: {e}") diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 75b53100c..46ec03d2e 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -75,19 +75,24 @@ def openai_client(client_with_models): return OpenAI(base_url=base_url, api_key="bar") +@pytest.fixture(params=["openai_client", "llama_stack_client"]) +def compat_client(request): + return request.getfixturevalue(request.param) + + @pytest.mark.parametrize( "test_case", [ "inference:completion:sanity", ], ) -def test_openai_completion_non_streaming(openai_client, client_with_models, text_model_id, test_case): +def test_openai_completion_non_streaming(llama_stack_client, client_with_models, text_model_id, test_case): skip_if_model_doesnt_support_openai_completion(client_with_models, text_model_id) tc = TestCase(test_case) # ollama needs more verbose prompting for some reason here... prompt = "Respond to this question and explain your answer. " + tc["content"] - response = openai_client.completions.create( + response = llama_stack_client.completions.create( model=text_model_id, prompt=prompt, stream=False, @@ -103,13 +108,13 @@ def test_openai_completion_non_streaming(openai_client, client_with_models, text "inference:completion:sanity", ], ) -def test_openai_completion_streaming(openai_client, client_with_models, text_model_id, test_case): +def test_openai_completion_streaming(llama_stack_client, client_with_models, text_model_id, test_case): skip_if_model_doesnt_support_openai_completion(client_with_models, text_model_id) tc = TestCase(test_case) # ollama needs more verbose prompting for some reason here... prompt = "Respond to this question and explain your answer. " + tc["content"] - response = openai_client.completions.create( + response = llama_stack_client.completions.create( model=text_model_id, prompt=prompt, stream=True, @@ -127,11 +132,11 @@ def test_openai_completion_streaming(openai_client, client_with_models, text_mod 0, ], ) -def test_openai_completion_prompt_logprobs(openai_client, client_with_models, text_model_id, prompt_logprobs): +def test_openai_completion_prompt_logprobs(llama_stack_client, client_with_models, text_model_id, prompt_logprobs): skip_if_provider_isnt_vllm(client_with_models, text_model_id) prompt = "Hello, world!" - response = openai_client.completions.create( + response = llama_stack_client.completions.create( model=text_model_id, prompt=prompt, stream=False, @@ -144,11 +149,11 @@ def test_openai_completion_prompt_logprobs(openai_client, client_with_models, te assert len(choice.prompt_logprobs) > 0 -def test_openai_completion_guided_choice(openai_client, client_with_models, text_model_id): +def test_openai_completion_guided_choice(llama_stack_client, client_with_models, text_model_id): skip_if_provider_isnt_vllm(client_with_models, text_model_id) prompt = "I am feeling really sad today." - response = openai_client.completions.create( + response = llama_stack_client.completions.create( model=text_model_id, prompt=prompt, stream=False, @@ -161,6 +166,9 @@ def test_openai_completion_guided_choice(openai_client, client_with_models, text assert choice.text in ["joy", "sadness"] +# Run the chat-completion tests with both the OpenAI client and the LlamaStack client + + @pytest.mark.parametrize( "test_case", [ @@ -168,13 +176,13 @@ def test_openai_completion_guided_choice(openai_client, client_with_models, text "inference:chat_completion:non_streaming_02", ], ) -def test_openai_chat_completion_non_streaming(openai_client, client_with_models, text_model_id, test_case): +def test_openai_chat_completion_non_streaming(compat_client, client_with_models, text_model_id, test_case): skip_if_model_doesnt_support_openai_chat_completion(client_with_models, text_model_id) tc = TestCase(test_case) question = tc["question"] expected = tc["expected"] - response = openai_client.chat.completions.create( + response = compat_client.chat.completions.create( model=text_model_id, messages=[ { @@ -196,13 +204,13 @@ def test_openai_chat_completion_non_streaming(openai_client, client_with_models, "inference:chat_completion:streaming_02", ], ) -def test_openai_chat_completion_streaming(openai_client, client_with_models, text_model_id, test_case): +def test_openai_chat_completion_streaming(compat_client, client_with_models, text_model_id, test_case): skip_if_model_doesnt_support_openai_chat_completion(client_with_models, text_model_id) tc = TestCase(test_case) question = tc["question"] expected = tc["expected"] - response = openai_client.chat.completions.create( + response = compat_client.chat.completions.create( model=text_model_id, messages=[{"role": "user", "content": question}], stream=True, diff --git a/tests/integration/post_training/test_post_training.py b/tests/integration/post_training/test_post_training.py index 3e22bc5a7..648ace9d6 100644 --- a/tests/integration/post_training/test_post_training.py +++ b/tests/integration/post_training/test_post_training.py @@ -3,7 +3,6 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import List import pytest @@ -77,7 +76,7 @@ class TestPostTraining: async def test_get_training_jobs(self, post_training_stack): post_training_impl = post_training_stack jobs_list = await post_training_impl.get_training_jobs() - assert isinstance(jobs_list, List) + assert isinstance(jobs_list, list) assert jobs_list[0].job_uuid == "1234" @pytest.mark.asyncio diff --git a/tests/integration/providers/nvidia/__init__.py b/tests/integration/providers/nvidia/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/tests/integration/providers/nvidia/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/tests/integration/providers/nvidia/conftest.py b/tests/integration/providers/nvidia/conftest.py new file mode 100644 index 000000000..8beb113b0 --- /dev/null +++ b/tests/integration/providers/nvidia/conftest.py @@ -0,0 +1,14 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import os + +import pytest + +# Skip all tests in this directory when running in GitHub Actions +in_github_actions = os.environ.get("GITHUB_ACTIONS") == "true" +if in_github_actions: + pytest.skip("Skipping NVIDIA tests in GitHub Actions environment", allow_module_level=True) diff --git a/tests/integration/providers/nvidia/test_datastore.py b/tests/integration/providers/nvidia/test_datastore.py new file mode 100644 index 000000000..5f96dee9f --- /dev/null +++ b/tests/integration/providers/nvidia/test_datastore.py @@ -0,0 +1,47 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + + +import pytest + +# How to run this test: +# +# LLAMA_STACK_CONFIG="nvidia" pytest -v tests/integration/providers/nvidia/test_datastore.py + + +# nvidia provider only +@pytest.mark.parametrize( + "provider_id", + [ + "nvidia", + ], +) +def test_register_and_unregister(llama_stack_client, provider_id): + purpose = "eval/messages-answer" + source = { + "type": "uri", + "uri": "hf://datasets/llamastack/simpleqa?split=train", + } + dataset_id = f"test-dataset-{provider_id}" + dataset = llama_stack_client.datasets.register( + dataset_id=dataset_id, + purpose=purpose, + source=source, + metadata={"provider_id": provider_id, "format": "json", "description": "Test dataset description"}, + ) + assert dataset.identifier is not None + assert dataset.provider_id == provider_id + assert dataset.identifier == dataset_id + + dataset_list = llama_stack_client.datasets.list() + provider_datasets = [d for d in dataset_list if d.provider_id == provider_id] + assert any(provider_datasets) + assert any(d.identifier == dataset_id for d in provider_datasets) + + llama_stack_client.datasets.unregister(dataset.identifier) + dataset_list = llama_stack_client.datasets.list() + provider_datasets = [d for d in dataset_list if d.identifier == dataset.identifier] + assert not any(provider_datasets) diff --git a/tests/integration/test_cases/openai/responses.json b/tests/integration/test_cases/openai/responses.json new file mode 100644 index 000000000..d17d0cd4f --- /dev/null +++ b/tests/integration/test_cases/openai/responses.json @@ -0,0 +1,37 @@ +{ + "non_streaming_01": { + "data": { + "question": "Which planet do humans live on?", + "expected": "Earth" + } + }, + "non_streaming_02": { + "data": { + "question": "Which planet has rings around it with a name starting with letter S?", + "expected": "Saturn" + } + }, + "streaming_01": { + "data": { + "question": "What's the name of the Sun in latin?", + "expected": "Sol" + } + }, + "streaming_02": { + "data": { + "question": "What is the name of the US captial?", + "expected": "Washington" + } + }, + "tools_web_search_01": { + "data": { + "input": "How many experts does the Llama 4 Maverick model have?", + "tools": [ + { + "type": "web_search" + } + ], + "expected": "128" + } + } +} diff --git a/tests/integration/test_cases/test_case.py b/tests/integration/test_cases/test_case.py index 8514f3046..fc3bf97c8 100644 --- a/tests/integration/test_cases/test_case.py +++ b/tests/integration/test_cases/test_case.py @@ -12,6 +12,7 @@ class TestCase: _apis = [ "inference/chat_completion", "inference/completion", + "openai/responses", ] _jsonblob = {} @@ -19,7 +20,7 @@ class TestCase: # loading all test cases if self._jsonblob == {}: for api in self._apis: - with open(pathlib.Path(__file__).parent / f"{api}.json", "r") as f: + with open(pathlib.Path(__file__).parent / f"{api}.json") as f: coloned = api.replace("/", ":") try: loaded = json.load(f) diff --git a/tests/integration/tool_runtime/test_registration.py b/tests/integration/tool_runtime/test_registration.py index e4241d813..b36237d05 100644 --- a/tests/integration/tool_runtime/test_registration.py +++ b/tests/integration/tool_runtime/test_registration.py @@ -114,7 +114,7 @@ def test_register_and_unregister_toolgroup(llama_stack_client, mcp_server): llama_stack_client.toolgroups.unregister(toolgroup_id=test_toolgroup_id) # Verify it is unregistered - with pytest.raises(ValueError, match=f"Tool group '{test_toolgroup_id}' not found"): + with pytest.raises(Exception, match=f"Tool group '{test_toolgroup_id}' not found"): llama_stack_client.toolgroups.get(toolgroup_id=test_toolgroup_id) # Verify tools are also unregistered diff --git a/tests/unit/cli/test_stack_config.py b/tests/unit/cli/test_stack_config.py index 312f58c09..d2b6f4b08 100644 --- a/tests/unit/cli/test_stack_config.py +++ b/tests/unit/cli/test_stack_config.py @@ -18,11 +18,11 @@ from llama_stack.distribution.configure import ( @pytest.fixture def up_to_date_config(): return yaml.safe_load( - """ - version: {version} + f""" + version: {LLAMA_STACK_RUN_CONFIG_VERSION} image_name: foo apis_to_serve: [] - built_at: {built_at} + built_at: {datetime.now().isoformat()} providers: inference: - provider_id: provider1 @@ -42,16 +42,16 @@ def up_to_date_config(): - provider_id: provider1 provider_type: inline::meta-reference config: {{}} - """.format(version=LLAMA_STACK_RUN_CONFIG_VERSION, built_at=datetime.now().isoformat()) + """ ) @pytest.fixture def old_config(): return yaml.safe_load( - """ + f""" image_name: foo - built_at: {built_at} + built_at: {datetime.now().isoformat()} apis_to_serve: [] routing_table: inference: @@ -82,7 +82,7 @@ def old_config(): telemetry: provider_type: noop config: {{}} - """.format(built_at=datetime.now().isoformat()) + """ ) diff --git a/tests/unit/distribution/routers/test_routing_tables.py b/tests/unit/distribution/routers/test_routing_tables.py new file mode 100644 index 000000000..305e53839 --- /dev/null +++ b/tests/unit/distribution/routers/test_routing_tables.py @@ -0,0 +1,314 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +# Unit tests for the routing tables + +from unittest.mock import AsyncMock + +import pytest + +from llama_stack.apis.common.type_system import NumberType +from llama_stack.apis.datasets.datasets import Dataset, DatasetPurpose, URIDataSource +from llama_stack.apis.datatypes import Api +from llama_stack.apis.models.models import Model, ModelType +from llama_stack.apis.shields.shields import Shield +from llama_stack.apis.tools import ListToolDefsResponse, ToolDef, ToolParameter +from llama_stack.apis.vector_dbs.vector_dbs import VectorDB +from llama_stack.distribution.routers.routing_tables import ( + BenchmarksRoutingTable, + DatasetsRoutingTable, + ModelsRoutingTable, + ScoringFunctionsRoutingTable, + ShieldsRoutingTable, + ToolGroupsRoutingTable, + VectorDBsRoutingTable, +) +from llama_stack.distribution.store.registry import CachedDiskDistributionRegistry +from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig +from llama_stack.providers.utils.kvstore.sqlite import SqliteKVStoreImpl + + +@pytest.fixture +async def dist_registry(tmp_path): + db_path = tmp_path / "test_kv.db" + kvstore_config = SqliteKVStoreConfig(db_path=db_path.as_posix()) + kvstore = SqliteKVStoreImpl(kvstore_config) + await kvstore.initialize() + registry = CachedDiskDistributionRegistry(kvstore) + await registry.initialize() + yield registry + + +class Impl: + def __init__(self, api: Api): + self.api = api + + @property + def __provider_spec__(self): + _provider_spec = AsyncMock() + _provider_spec.api = self.api + return _provider_spec + + +class InferenceImpl(Impl): + def __init__(self): + super().__init__(Api.inference) + + async def register_model(self, model: Model): + return model + + async def unregister_model(self, model_id: str): + return model_id + + +class SafetyImpl(Impl): + def __init__(self): + super().__init__(Api.safety) + + async def register_shield(self, shield: Shield): + return shield + + +class VectorDBImpl(Impl): + def __init__(self): + super().__init__(Api.vector_io) + + async def register_vector_db(self, vector_db: VectorDB): + return vector_db + + async def unregister_vector_db(self, vector_db_id: str): + return vector_db_id + + +class DatasetsImpl(Impl): + def __init__(self): + super().__init__(Api.datasetio) + + async def register_dataset(self, dataset: Dataset): + return dataset + + async def unregister_dataset(self, dataset_id: str): + return dataset_id + + +class ScoringFunctionsImpl(Impl): + def __init__(self): + super().__init__(Api.scoring) + + async def list_scoring_functions(self): + return [] + + async def register_scoring_function(self, scoring_fn): + return scoring_fn + + +class BenchmarksImpl(Impl): + def __init__(self): + super().__init__(Api.eval) + + async def register_benchmark(self, benchmark): + return benchmark + + +class ToolGroupsImpl(Impl): + def __init__(self): + super().__init__(Api.tool_runtime) + + async def register_tool(self, tool): + return tool + + async def unregister_tool(self, tool_name: str): + return tool_name + + async def list_runtime_tools(self, toolgroup_id, mcp_endpoint): + return ListToolDefsResponse( + data=[ + ToolDef( + name="test-tool", + description="Test tool", + parameters=[ToolParameter(name="test-param", description="Test param", parameter_type="string")], + ) + ] + ) + + +@pytest.mark.asyncio +async def test_models_routing_table(dist_registry): + table = ModelsRoutingTable({"test_provider": InferenceImpl()}, dist_registry) + await table.initialize() + + # Register multiple models and verify listing + await table.register_model(model_id="test-model", provider_id="test_provider") + await table.register_model(model_id="test-model-2", provider_id="test_provider") + + models = await table.list_models() + assert len(models.data) == 2 + model_ids = {m.identifier for m in models.data} + assert "test-model" in model_ids + assert "test-model-2" in model_ids + + # Test openai list models + openai_models = await table.openai_list_models() + assert len(openai_models.data) == 2 + openai_model_ids = {m.id for m in openai_models.data} + assert "test-model" in openai_model_ids + assert "test-model-2" in openai_model_ids + + # Test get_object_by_identifier + model = await table.get_object_by_identifier("model", "test-model") + assert model is not None + assert model.identifier == "test-model" + + # Test get_object_by_identifier on non-existent object + non_existent = await table.get_object_by_identifier("model", "non-existent-model") + assert non_existent is None + + await table.unregister_model(model_id="test-model") + await table.unregister_model(model_id="test-model-2") + + models = await table.list_models() + assert len(models.data) == 0 + + # Test openai list models + openai_models = await table.openai_list_models() + assert len(openai_models.data) == 0 + + +@pytest.mark.asyncio +async def test_shields_routing_table(dist_registry): + table = ShieldsRoutingTable({"test_provider": SafetyImpl()}, dist_registry) + await table.initialize() + + # Register multiple shields and verify listing + await table.register_shield(shield_id="test-shield", provider_id="test_provider") + await table.register_shield(shield_id="test-shield-2", provider_id="test_provider") + shields = await table.list_shields() + + assert len(shields.data) == 2 + shield_ids = {s.identifier for s in shields.data} + assert "test-shield" in shield_ids + assert "test-shield-2" in shield_ids + + +@pytest.mark.asyncio +async def test_vectordbs_routing_table(dist_registry): + table = VectorDBsRoutingTable({"test_provider": VectorDBImpl()}, dist_registry) + await table.initialize() + + m_table = ModelsRoutingTable({"test_providere": InferenceImpl()}, dist_registry) + await m_table.initialize() + await m_table.register_model( + model_id="test-model", + provider_id="test_providere", + metadata={"embedding_dimension": 128}, + model_type=ModelType.embedding, + ) + + # Register multiple vector databases and verify listing + await table.register_vector_db(vector_db_id="test-vectordb", embedding_model="test-model") + await table.register_vector_db(vector_db_id="test-vectordb-2", embedding_model="test-model") + vector_dbs = await table.list_vector_dbs() + + assert len(vector_dbs.data) == 2 + vector_db_ids = {v.identifier for v in vector_dbs.data} + assert "test-vectordb" in vector_db_ids + assert "test-vectordb-2" in vector_db_ids + + await table.unregister_vector_db(vector_db_id="test-vectordb") + await table.unregister_vector_db(vector_db_id="test-vectordb-2") + + vector_dbs = await table.list_vector_dbs() + assert len(vector_dbs.data) == 0 + + +async def test_datasets_routing_table(dist_registry): + table = DatasetsRoutingTable({"localfs": DatasetsImpl()}, dist_registry) + await table.initialize() + + # Register multiple datasets and verify listing + await table.register_dataset( + dataset_id="test-dataset", purpose=DatasetPurpose.eval_messages_answer, source=URIDataSource(uri="test-uri") + ) + await table.register_dataset( + dataset_id="test-dataset-2", purpose=DatasetPurpose.eval_messages_answer, source=URIDataSource(uri="test-uri-2") + ) + datasets = await table.list_datasets() + + assert len(datasets.data) == 2 + dataset_ids = {d.identifier for d in datasets.data} + assert "test-dataset" in dataset_ids + assert "test-dataset-2" in dataset_ids + + await table.unregister_dataset(dataset_id="test-dataset") + await table.unregister_dataset(dataset_id="test-dataset-2") + + datasets = await table.list_datasets() + assert len(datasets.data) == 0 + + +@pytest.mark.asyncio +async def test_scoring_functions_routing_table(dist_registry): + table = ScoringFunctionsRoutingTable({"test_provider": ScoringFunctionsImpl()}, dist_registry) + await table.initialize() + + # Register multiple scoring functions and verify listing + await table.register_scoring_function( + scoring_fn_id="test-scoring-fn", + provider_id="test_provider", + description="Test scoring function", + return_type=NumberType(), + ) + await table.register_scoring_function( + scoring_fn_id="test-scoring-fn-2", + provider_id="test_provider", + description="Another test scoring function", + return_type=NumberType(), + ) + scoring_functions = await table.list_scoring_functions() + + assert len(scoring_functions.data) == 2 + scoring_fn_ids = {fn.identifier for fn in scoring_functions.data} + assert "test-scoring-fn" in scoring_fn_ids + assert "test-scoring-fn-2" in scoring_fn_ids + + +@pytest.mark.asyncio +async def test_benchmarks_routing_table(dist_registry): + table = BenchmarksRoutingTable({"test_provider": BenchmarksImpl()}, dist_registry) + await table.initialize() + + # Register multiple benchmarks and verify listing + await table.register_benchmark( + benchmark_id="test-benchmark", + dataset_id="test-dataset", + scoring_functions=["test-scoring-fn", "test-scoring-fn-2"], + ) + benchmarks = await table.list_benchmarks() + + assert len(benchmarks.data) == 1 + benchmark_ids = {b.identifier for b in benchmarks.data} + assert "test-benchmark" in benchmark_ids + + +@pytest.mark.asyncio +async def test_tool_groups_routing_table(dist_registry): + table = ToolGroupsRoutingTable({"test_provider": ToolGroupsImpl()}, dist_registry) + await table.initialize() + + # Register multiple tool groups and verify listing + await table.register_tool_group( + toolgroup_id="test-toolgroup", + provider_id="test_provider", + ) + tool_groups = await table.list_tool_groups() + + assert len(tool_groups.data) == 1 + tool_group_ids = {tg.identifier for tg in tool_groups.data} + assert "test-toolgroup" in tool_group_ids + + await table.unregister_toolgroup(toolgroup_id="test-toolgroup") + tool_groups = await table.list_tool_groups() + assert len(tool_groups.data) == 0 diff --git a/tests/unit/distribution/test_build_path.py b/tests/unit/distribution/test_build_path.py new file mode 100644 index 000000000..555cdda4a --- /dev/null +++ b/tests/unit/distribution/test_build_path.py @@ -0,0 +1,40 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from pathlib import Path + +from llama_stack.cli.stack._build import ( + _run_stack_build_command_from_build_config, +) +from llama_stack.distribution.datatypes import BuildConfig, DistributionSpec +from llama_stack.distribution.utils.image_types import LlamaStackImageType + + +def test_container_build_passes_path(monkeypatch, tmp_path): + called_with = {} + + def spy_build_image(cfg, build_file_path, image_name, template_or_config, run_config=None): + called_with["path"] = template_or_config + called_with["run_config"] = run_config + return 0 + + monkeypatch.setattr( + "llama_stack.cli.stack._build.build_image", + spy_build_image, + raising=True, + ) + + cfg = BuildConfig( + image_type=LlamaStackImageType.CONTAINER.value, + distribution_spec=DistributionSpec(providers={}, description=""), + ) + + _run_stack_build_command_from_build_config(cfg, image_name="dummy") + + assert "path" in called_with + assert isinstance(called_with["path"], str) + assert Path(called_with["path"]).exists() + assert called_with["run_config"] is None diff --git a/tests/unit/distribution/test_distribution.py b/tests/unit/distribution/test_distribution.py index a4daffb82..ae24602d7 100644 --- a/tests/unit/distribution/test_distribution.py +++ b/tests/unit/distribution/test_distribution.py @@ -4,7 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Any, Dict +from typing import Any from unittest.mock import patch import pytest @@ -23,7 +23,7 @@ class SampleConfig(BaseModel): ) @classmethod - def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs: Any) -> dict[str, Any]: return { "foo": "baz", } diff --git a/tests/unit/providers/agents/meta_reference/test_openai_responses.py b/tests/unit/providers/agents/meta_reference/test_openai_responses.py new file mode 100644 index 000000000..cd6b0fc55 --- /dev/null +++ b/tests/unit/providers/agents/meta_reference/test_openai_responses.py @@ -0,0 +1,202 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from unittest.mock import AsyncMock + +import pytest + +from llama_stack.apis.agents.openai_responses import ( + OpenAIResponseInputToolWebSearch, + OpenAIResponseOutputMessage, +) +from llama_stack.apis.inference.inference import ( + OpenAIAssistantMessageParam, + OpenAIChatCompletion, + OpenAIChatCompletionToolCall, + OpenAIChatCompletionToolCallFunction, + OpenAIChoice, + OpenAIUserMessageParam, +) +from llama_stack.apis.tools.tools import Tool, ToolGroups, ToolInvocationResult, ToolParameter, ToolRuntime +from llama_stack.providers.inline.agents.meta_reference.openai_responses import ( + OpenAIResponsesImpl, +) +from llama_stack.providers.utils.kvstore import KVStore + + +@pytest.fixture +def mock_kvstore(): + kvstore = AsyncMock(spec=KVStore) + return kvstore + + +@pytest.fixture +def mock_inference_api(): + inference_api = AsyncMock() + return inference_api + + +@pytest.fixture +def mock_tool_groups_api(): + tool_groups_api = AsyncMock(spec=ToolGroups) + return tool_groups_api + + +@pytest.fixture +def mock_tool_runtime_api(): + tool_runtime_api = AsyncMock(spec=ToolRuntime) + return tool_runtime_api + + +@pytest.fixture +def openai_responses_impl(mock_kvstore, mock_inference_api, mock_tool_groups_api, mock_tool_runtime_api): + return OpenAIResponsesImpl( + persistence_store=mock_kvstore, + inference_api=mock_inference_api, + tool_groups_api=mock_tool_groups_api, + tool_runtime_api=mock_tool_runtime_api, + ) + + +@pytest.mark.asyncio +async def test_create_openai_response_with_string_input(openai_responses_impl, mock_inference_api): + """Test creating an OpenAI response with a simple string input.""" + # Setup + input_text = "Hello, world!" + model = "meta-llama/Llama-3.1-8B-Instruct" + + mock_chat_completion = OpenAIChatCompletion( + id="chat-completion-123", + choices=[ + OpenAIChoice( + message=OpenAIAssistantMessageParam(content="Hello! How can I help you?"), + finish_reason="stop", + index=0, + ) + ], + created=1234567890, + model=model, + ) + mock_inference_api.openai_chat_completion.return_value = mock_chat_completion + + # Execute + result = await openai_responses_impl.create_openai_response( + input=input_text, + model=model, + temperature=0.1, + ) + + # Verify + mock_inference_api.openai_chat_completion.assert_called_once_with( + model=model, + messages=[OpenAIUserMessageParam(role="user", content="Hello, world!", name=None)], + tools=None, + stream=False, + temperature=0.1, + ) + openai_responses_impl.persistence_store.set.assert_called_once() + assert result.model == model + assert len(result.output) == 1 + assert isinstance(result.output[0], OpenAIResponseOutputMessage) + assert result.output[0].content[0].text == "Hello! How can I help you?" + + +@pytest.mark.asyncio +async def test_create_openai_response_with_string_input_with_tools(openai_responses_impl, mock_inference_api): + """Test creating an OpenAI response with a simple string input and tools.""" + # Setup + input_text = "What was the score of todays game?" + model = "meta-llama/Llama-3.1-8B-Instruct" + + mock_chat_completions = [ + OpenAIChatCompletion( + id="chat-completion-123", + choices=[ + OpenAIChoice( + message=OpenAIAssistantMessageParam( + tool_calls=[ + OpenAIChatCompletionToolCall( + id="tool_call_123", + type="function", + function=OpenAIChatCompletionToolCallFunction( + name="web_search", arguments='{"query":"What was the score of todays game?"}' + ), + ) + ], + ), + finish_reason="stop", + index=0, + ) + ], + created=1234567890, + model=model, + ), + OpenAIChatCompletion( + id="chat-completion-123", + choices=[ + OpenAIChoice( + message=OpenAIAssistantMessageParam(content="The score of todays game was 10-12"), + finish_reason="stop", + index=0, + ) + ], + created=1234567890, + model=model, + ), + ] + + mock_inference_api.openai_chat_completion.side_effect = mock_chat_completions + + openai_responses_impl.tool_groups_api.get_tool.return_value = Tool( + identifier="web_search", + provider_id="client", + toolgroup_id="web_search", + tool_host="client", + description="Search the web for information", + parameters=[ + ToolParameter(name="query", parameter_type="string", description="The query to search for", required=True) + ], + ) + + openai_responses_impl.tool_runtime_api.invoke_tool.return_value = ToolInvocationResult( + status="completed", + content="The score of todays game was 10-12", + ) + + # Execute + result = await openai_responses_impl.create_openai_response( + input=input_text, + model=model, + temperature=0.1, + tools=[ + OpenAIResponseInputToolWebSearch( + name="web_search", + ) + ], + ) + + # Verify + first_call = mock_inference_api.openai_chat_completion.call_args_list[0] + assert first_call.kwargs["messages"][0].content == "What was the score of todays game?" + assert first_call.kwargs["tools"] is not None + assert first_call.kwargs["temperature"] == 0.1 + + second_call = mock_inference_api.openai_chat_completion.call_args_list[1] + assert second_call.kwargs["messages"][-1].content == "The score of todays game was 10-12" + assert second_call.kwargs["temperature"] == 0.1 + + openai_responses_impl.tool_groups_api.get_tool.assert_called_once_with("web_search") + openai_responses_impl.tool_runtime_api.invoke_tool.assert_called_once_with( + tool_name="web_search", + kwargs={"query": "What was the score of todays game?"}, + ) + + openai_responses_impl.persistence_store.set.assert_called_once() + + # Check that we got the content from our mocked tool execution result + assert len(result.output) >= 1 + assert isinstance(result.output[1], OpenAIResponseOutputMessage) + assert result.output[1].content[0].text == "The score of todays game was 10-12" diff --git a/tests/unit/providers/inference/test_remote_vllm.py b/tests/unit/providers/inference/test_remote_vllm.py index 9c2281d85..a2e3b64c2 100644 --- a/tests/unit/providers/inference/test_remote_vllm.py +++ b/tests/unit/providers/inference/test_remote_vllm.py @@ -10,7 +10,7 @@ import logging import threading import time from http.server import BaseHTTPRequestHandler, HTTPServer -from typing import Any, Dict +from typing import Any from unittest.mock import AsyncMock, patch import pytest @@ -26,9 +26,17 @@ from openai.types.chat.chat_completion_chunk import ( ) from openai.types.model import Model as OpenAIModel -from llama_stack.apis.inference import ToolChoice, ToolConfig +from llama_stack.apis.inference import ( + ChatCompletionRequest, + CompletionMessage, + SystemMessage, + ToolChoice, + ToolConfig, + ToolResponseMessage, + UserMessage, +) from llama_stack.apis.models import Model -from llama_stack.models.llama.datatypes import StopReason +from llama_stack.models.llama.datatypes import StopReason, ToolCall from llama_stack.providers.remote.inference.vllm.config import VLLMInferenceAdapterConfig from llama_stack.providers.remote.inference.vllm.vllm import ( VLLMInferenceAdapter, @@ -47,7 +55,7 @@ from llama_stack.providers.remote.inference.vllm.vllm import ( class MockInferenceAdapterWithSleep: - def __init__(self, sleep_time: int, response: Dict[str, Any]): + def __init__(self, sleep_time: int, response: dict[str, Any]): self.httpd = None class DelayedRequestHandler(BaseHTTPRequestHandler): @@ -130,6 +138,49 @@ async def test_old_vllm_tool_choice(vllm_inference_adapter): assert request.tool_config.tool_choice == ToolChoice.none +@pytest.mark.asyncio +async def test_tool_call_response(vllm_inference_adapter): + """Verify that tool call arguments from a CompletionMessage are correctly converted + into the expected JSON format.""" + + # Patch the call to vllm so we can inspect the arguments sent were correct + with patch.object( + vllm_inference_adapter.client.chat.completions, "create", new_callable=AsyncMock + ) as mock_nonstream_completion: + messages = [ + SystemMessage(content="You are a helpful assistant"), + UserMessage(content="How many?"), + CompletionMessage( + content="", + stop_reason=StopReason.end_of_turn, + tool_calls=[ + ToolCall( + call_id="foo", + tool_name="knowledge_search", + arguments={"query": "How many?"}, + arguments_json='{"query": "How many?"}', + ) + ], + ), + ToolResponseMessage(call_id="foo", content="knowledge_search found 5...."), + ] + await vllm_inference_adapter.chat_completion( + "mock-model", + messages, + stream=False, + tools=[], + tool_config=ToolConfig(tool_choice=ToolChoice.auto), + ) + + assert mock_nonstream_completion.call_args.kwargs["messages"][2]["tool_calls"] == [ + { + "id": "foo", + "type": "function", + "function": {"name": "knowledge_search", "arguments": '{"query": "How many?"}'}, + } + ] + + @pytest.mark.asyncio async def test_tool_call_delta_empty_tool_call_buf(): """ @@ -232,3 +283,14 @@ def test_chat_completion_doesnt_block_event_loop(caplog): # above. asyncio_warnings = [record.message for record in caplog.records if record.name == "asyncio"] assert not asyncio_warnings + + +@pytest.mark.asyncio +async def test_get_params_empty_tools(vllm_inference_adapter): + request = ChatCompletionRequest( + tools=[], + model="test_model", + messages=[UserMessage(content="test")], + ) + params = await vllm_inference_adapter._get_params(request) + assert "tools" not in params diff --git a/tests/unit/providers/nvidia/test_datastore.py b/tests/unit/providers/nvidia/test_datastore.py new file mode 100644 index 000000000..a17e51a9c --- /dev/null +++ b/tests/unit/providers/nvidia/test_datastore.py @@ -0,0 +1,138 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import os +import unittest +from unittest.mock import patch + +import pytest + +from llama_stack.apis.datasets import Dataset, DatasetPurpose, URIDataSource +from llama_stack.providers.remote.datasetio.nvidia.config import NvidiaDatasetIOConfig +from llama_stack.providers.remote.datasetio.nvidia.datasetio import NvidiaDatasetIOAdapter + + +class TestNvidiaDatastore(unittest.TestCase): + def setUp(self): + os.environ["NVIDIA_DATASETS_URL"] = "http://nemo.test/datasets" + + config = NvidiaDatasetIOConfig( + datasets_url=os.environ["NVIDIA_DATASETS_URL"], dataset_namespace="default", project_id="default" + ) + self.adapter = NvidiaDatasetIOAdapter(config) + self.make_request_patcher = patch( + "llama_stack.providers.remote.datasetio.nvidia.datasetio.NvidiaDatasetIOAdapter._make_request" + ) + self.mock_make_request = self.make_request_patcher.start() + + def tearDown(self): + self.make_request_patcher.stop() + + @pytest.fixture(autouse=True) + def inject_fixtures(self, run_async): + self.run_async = run_async + + def _assert_request(self, mock_call, expected_method, expected_path, expected_json=None): + """Helper method to verify request details in mock calls.""" + call_args = mock_call.call_args + + assert call_args[0][0] == expected_method + assert call_args[0][1] == expected_path + + if expected_json: + for key, value in expected_json.items(): + assert call_args[1]["json"][key] == value + + def test_register_dataset(self): + self.mock_make_request.return_value = { + "id": "dataset-123456", + "name": "test-dataset", + "namespace": "default", + } + + dataset_def = Dataset( + identifier="test-dataset", + type="dataset", + provider_resource_id="", + provider_id="", + purpose=DatasetPurpose.post_training_messages, + source=URIDataSource(uri="https://example.com/data.jsonl"), + metadata={"provider_id": "nvidia", "format": "jsonl", "description": "Test dataset description"}, + ) + + self.run_async(self.adapter.register_dataset(dataset_def)) + + self.mock_make_request.assert_called_once() + self._assert_request( + self.mock_make_request, + "POST", + "/v1/datasets", + expected_json={ + "name": "test-dataset", + "namespace": "default", + "files_url": "https://example.com/data.jsonl", + "project": "default", + "format": "jsonl", + "description": "Test dataset description", + }, + ) + + def test_unregister_dataset(self): + self.mock_make_request.return_value = { + "message": "Resource deleted successfully.", + "id": "dataset-81RSQp7FKX3rdBtKvF9Skn", + "deleted_at": None, + } + dataset_id = "test-dataset" + + self.run_async(self.adapter.unregister_dataset(dataset_id)) + + self.mock_make_request.assert_called_once() + self._assert_request(self.mock_make_request, "DELETE", "/v1/datasets/default/test-dataset") + + def test_register_dataset_with_custom_namespace_project(self): + custom_config = NvidiaDatasetIOConfig( + datasets_url=os.environ["NVIDIA_DATASETS_URL"], + dataset_namespace="custom-namespace", + project_id="custom-project", + ) + custom_adapter = NvidiaDatasetIOAdapter(custom_config) + + self.mock_make_request.return_value = { + "id": "dataset-123456", + "name": "test-dataset", + "namespace": "custom-namespace", + } + + dataset_def = Dataset( + identifier="test-dataset", + type="dataset", + provider_resource_id="", + provider_id="", + purpose=DatasetPurpose.post_training_messages, + source=URIDataSource(uri="https://example.com/data.jsonl"), + metadata={"format": "jsonl"}, + ) + + self.run_async(custom_adapter.register_dataset(dataset_def)) + + self.mock_make_request.assert_called_once() + self._assert_request( + self.mock_make_request, + "POST", + "/v1/datasets", + expected_json={ + "name": "test-dataset", + "namespace": "custom-namespace", + "files_url": "https://example.com/data.jsonl", + "project": "custom-project", + "format": "jsonl", + }, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/unit/providers/nvidia/test_eval.py b/tests/unit/providers/nvidia/test_eval.py new file mode 100644 index 000000000..584ca2101 --- /dev/null +++ b/tests/unit/providers/nvidia/test_eval.py @@ -0,0 +1,201 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import os +import unittest +from unittest.mock import MagicMock, patch + +import pytest + +from llama_stack.apis.benchmarks import Benchmark +from llama_stack.apis.common.job_types import Job, JobStatus +from llama_stack.apis.eval.eval import BenchmarkConfig, EvaluateResponse, ModelCandidate, SamplingParams +from llama_stack.models.llama.sku_types import CoreModelId +from llama_stack.providers.remote.eval.nvidia.config import NVIDIAEvalConfig +from llama_stack.providers.remote.eval.nvidia.eval import NVIDIAEvalImpl + +MOCK_DATASET_ID = "default/test-dataset" +MOCK_BENCHMARK_ID = "test-benchmark" + + +class TestNVIDIAEvalImpl(unittest.TestCase): + def setUp(self): + os.environ["NVIDIA_EVALUATOR_URL"] = "http://nemo.test" + + # Create mock APIs + self.datasetio_api = MagicMock() + self.datasets_api = MagicMock() + self.scoring_api = MagicMock() + self.inference_api = MagicMock() + self.agents_api = MagicMock() + + self.config = NVIDIAEvalConfig( + evaluator_url=os.environ["NVIDIA_EVALUATOR_URL"], + ) + + self.eval_impl = NVIDIAEvalImpl( + config=self.config, + datasetio_api=self.datasetio_api, + datasets_api=self.datasets_api, + scoring_api=self.scoring_api, + inference_api=self.inference_api, + agents_api=self.agents_api, + ) + + # Mock the HTTP request methods + self.evaluator_get_patcher = patch( + "llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_get" + ) + self.evaluator_post_patcher = patch( + "llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_post" + ) + + self.mock_evaluator_get = self.evaluator_get_patcher.start() + self.mock_evaluator_post = self.evaluator_post_patcher.start() + + def tearDown(self): + """Clean up after each test.""" + self.evaluator_get_patcher.stop() + self.evaluator_post_patcher.stop() + + def _assert_request_body(self, expected_json): + """Helper method to verify request body in Evaluator POST request is correct""" + call_args = self.mock_evaluator_post.call_args + actual_json = call_args[0][1] + + # Check that all expected keys contain the expected values in the actual JSON + for key, value in expected_json.items(): + assert key in actual_json, f"Key '{key}' missing in actual JSON" + + if isinstance(value, dict): + for nested_key, nested_value in value.items(): + assert nested_key in actual_json[key], f"Nested key '{nested_key}' missing in actual JSON['{key}']" + assert actual_json[key][nested_key] == nested_value, f"Value mismatch for '{key}.{nested_key}'" + else: + assert actual_json[key] == value, f"Value mismatch for '{key}'" + + @pytest.fixture(autouse=True) + def inject_fixtures(self, run_async): + self.run_async = run_async + + def test_register_benchmark(self): + eval_config = { + "type": "custom", + "params": {"parallelism": 8}, + "tasks": { + "qa": { + "type": "completion", + "params": {"template": {"prompt": "{{prompt}}", "max_tokens": 200}}, + "dataset": {"files_url": f"hf://datasets/{MOCK_DATASET_ID}/testing/testing.jsonl"}, + "metrics": {"bleu": {"type": "bleu", "params": {"references": ["{{ideal_response}}"]}}}, + } + }, + } + + benchmark = Benchmark( + provider_id="nvidia", + type="benchmark", + identifier=MOCK_BENCHMARK_ID, + dataset_id=MOCK_DATASET_ID, + scoring_functions=["basic::equality"], + metadata=eval_config, + ) + + # Mock Evaluator API response + mock_evaluator_response = {"id": MOCK_BENCHMARK_ID, "status": "created"} + self.mock_evaluator_post.return_value = mock_evaluator_response + + # Register the benchmark + self.run_async(self.eval_impl.register_benchmark(benchmark)) + + # Verify the Evaluator API was called correctly + self.mock_evaluator_post.assert_called_once() + self._assert_request_body({"namespace": benchmark.provider_id, "name": benchmark.identifier, **eval_config}) + + def test_run_eval(self): + benchmark_config = BenchmarkConfig( + eval_candidate=ModelCandidate( + type="model", + model=CoreModelId.llama3_1_8b_instruct.value, + sampling_params=SamplingParams(max_tokens=100, temperature=0.7), + ) + ) + + # Mock Evaluator API response + mock_evaluator_response = {"id": "job-123", "status": "created"} + self.mock_evaluator_post.return_value = mock_evaluator_response + + # Run the Evaluation job + result = self.run_async( + self.eval_impl.run_eval(benchmark_id=MOCK_BENCHMARK_ID, benchmark_config=benchmark_config) + ) + + # Verify the Evaluator API was called correctly + self.mock_evaluator_post.assert_called_once() + self._assert_request_body( + { + "config": f"nvidia/{MOCK_BENCHMARK_ID}", + "target": {"type": "model", "model": "meta/llama-3.1-8b-instruct"}, + } + ) + + # Verify the result + assert isinstance(result, Job) + assert result.job_id == "job-123" + assert result.status == JobStatus.in_progress + + def test_job_status(self): + # Mock Evaluator API response + mock_evaluator_response = {"id": "job-123", "status": "completed"} + self.mock_evaluator_get.return_value = mock_evaluator_response + + # Get the Evaluation job + result = self.run_async(self.eval_impl.job_status(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123")) + + # Verify the result + assert isinstance(result, Job) + assert result.job_id == "job-123" + assert result.status == JobStatus.completed + + # Verify the API was called correctly + self.mock_evaluator_get.assert_called_once_with(f"/v1/evaluation/jobs/{result.job_id}") + + def test_job_cancel(self): + # Mock Evaluator API response + mock_evaluator_response = {"id": "job-123", "status": "cancelled"} + self.mock_evaluator_post.return_value = mock_evaluator_response + + # Cancel the Evaluation job + self.run_async(self.eval_impl.job_cancel(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123")) + + # Verify the API was called correctly + self.mock_evaluator_post.assert_called_once_with("/v1/evaluation/jobs/job-123/cancel", {}) + + def test_job_result(self): + # Mock Evaluator API responses + mock_job_status_response = {"id": "job-123", "status": "completed"} + mock_job_results_response = { + "id": "job-123", + "status": "completed", + "results": {MOCK_BENCHMARK_ID: {"score": 0.85, "details": {"accuracy": 0.85, "f1": 0.84}}}, + } + self.mock_evaluator_get.side_effect = [ + mock_job_status_response, # First call to retrieve job + mock_job_results_response, # Second call to retrieve job results + ] + + # Get the Evaluation job results + result = self.run_async(self.eval_impl.job_result(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123")) + + # Verify the result + assert isinstance(result, EvaluateResponse) + assert MOCK_BENCHMARK_ID in result.scores + assert result.scores[MOCK_BENCHMARK_ID].aggregated_results["results"][MOCK_BENCHMARK_ID]["score"] == 0.85 + + # Verify the API was called correctly + assert self.mock_evaluator_get.call_count == 2 + self.mock_evaluator_get.assert_any_call("/v1/evaluation/jobs/job-123") + self.mock_evaluator_get.assert_any_call("/v1/evaluation/jobs/job-123/results") diff --git a/tests/unit/providers/nvidia/test_parameters.py b/tests/unit/providers/nvidia/test_parameters.py index cb1b92fba..ea12122a0 100644 --- a/tests/unit/providers/nvidia/test_parameters.py +++ b/tests/unit/providers/nvidia/test_parameters.py @@ -10,14 +10,17 @@ import warnings from unittest.mock import patch import pytest -from llama_stack_client.types.algorithm_config_param import LoraFinetuningConfig -from llama_stack_client.types.post_training_supervised_fine_tune_params import ( - TrainingConfig, - TrainingConfigDataConfig, - TrainingConfigEfficiencyConfig, - TrainingConfigOptimizerConfig, -) +from llama_stack.apis.post_training.post_training import ( + DataConfig, + DatasetFormat, + EfficiencyConfig, + LoraFinetuningConfig, + OptimizerConfig, + OptimizerType, + TrainingConfig, +) +from llama_stack.distribution.library_client import convert_pydantic_to_json_value from llama_stack.providers.remote.post_training.nvidia.post_training import ( NvidiaPostTrainingAdapter, NvidiaPostTrainingConfig, @@ -66,11 +69,8 @@ class TestNvidiaParameters(unittest.TestCase): def test_customizer_parameters_passed(self): """Test scenario 1: When an optional parameter is passed and value is correctly set.""" - custom_adapter_dim = 32 # Different from default of 8 algorithm_config = LoraFinetuningConfig( type="LoRA", - adapter_dim=custom_adapter_dim, - adapter_dropout=0.2, apply_lora_to_mlp=True, apply_lora_to_output=True, alpha=16, @@ -78,8 +78,15 @@ class TestNvidiaParameters(unittest.TestCase): lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"], ) - data_config = TrainingConfigDataConfig(dataset_id="test-dataset", batch_size=16) - optimizer_config = TrainingConfigOptimizerConfig(lr=0.0002) + data_config = DataConfig( + dataset_id="test-dataset", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct + ) + optimizer_config = OptimizerConfig( + optimizer_type=OptimizerType.adam, + lr=0.0002, + weight_decay=0.01, + num_warmup_steps=100, + ) training_config = TrainingConfig( n_epochs=3, data_config=data_config, @@ -95,7 +102,7 @@ class TestNvidiaParameters(unittest.TestCase): model="meta-llama/Llama-3.1-8B-Instruct", checkpoint_dir="", algorithm_config=algorithm_config, - training_config=training_config, + training_config=convert_pydantic_to_json_value(training_config), logger_config={}, hyperparam_search_config={}, ) @@ -114,7 +121,7 @@ class TestNvidiaParameters(unittest.TestCase): self._assert_request_params( { "hyperparameters": { - "lora": {"adapter_dim": custom_adapter_dim, "adapter_dropout": 0.2, "alpha": 16}, + "lora": {"alpha": 16}, "epochs": 3, "learning_rate": 0.0002, "batch_size": 16, @@ -130,8 +137,6 @@ class TestNvidiaParameters(unittest.TestCase): algorithm_config = LoraFinetuningConfig( type="LoRA", - adapter_dim=16, - adapter_dropout=0.1, apply_lora_to_mlp=True, apply_lora_to_output=True, alpha=16, @@ -139,12 +144,16 @@ class TestNvidiaParameters(unittest.TestCase): lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"], ) - data_config = TrainingConfigDataConfig( - dataset_id=required_dataset_id, # Required parameter - batch_size=8, + data_config = DataConfig( + dataset_id=required_dataset_id, batch_size=8, shuffle=False, data_format=DatasetFormat.instruct ) - optimizer_config = TrainingConfigOptimizerConfig(lr=0.0001) + optimizer_config = OptimizerConfig( + optimizer_type=OptimizerType.adam, + lr=0.0001, + weight_decay=0.01, + num_warmup_steps=100, + ) training_config = TrainingConfig( n_epochs=1, @@ -161,7 +170,7 @@ class TestNvidiaParameters(unittest.TestCase): model=required_model, # Required parameter checkpoint_dir="", algorithm_config=algorithm_config, - training_config=training_config, + training_config=convert_pydantic_to_json_value(training_config), logger_config={}, hyperparam_search_config={}, ) @@ -186,24 +195,24 @@ class TestNvidiaParameters(unittest.TestCase): def test_unsupported_parameters_warning(self): """Test that warnings are raised for unsupported parameters.""" - data_config = TrainingConfigDataConfig( + data_config = DataConfig( dataset_id="test-dataset", batch_size=8, # Unsupported parameters shuffle=True, - data_format="instruct", + data_format=DatasetFormat.instruct, validation_dataset_id="val-dataset", ) - optimizer_config = TrainingConfigOptimizerConfig( + optimizer_config = OptimizerConfig( lr=0.0001, weight_decay=0.01, # Unsupported parameters - optimizer_type="adam", + optimizer_type=OptimizerType.adam, num_warmup_steps=100, ) - efficiency_config = TrainingConfigEfficiencyConfig( + efficiency_config = EfficiencyConfig( enable_activation_checkpointing=True # Unsupported parameter ) @@ -230,15 +239,13 @@ class TestNvidiaParameters(unittest.TestCase): checkpoint_dir="test-dir", # Unsupported parameter algorithm_config=LoraFinetuningConfig( type="LoRA", - adapter_dim=16, - adapter_dropout=0.1, apply_lora_to_mlp=True, apply_lora_to_output=True, alpha=16, rank=16, lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"], ), - training_config=training_config, + training_config=convert_pydantic_to_json_value(training_config), logger_config={"test": "value"}, # Unsupported parameter hyperparam_search_config={"test": "value"}, # Unsupported parameter ) diff --git a/tests/unit/providers/nvidia/test_safety.py b/tests/unit/providers/nvidia/test_safety.py index e7e1cb3dc..8c74f178b 100644 --- a/tests/unit/providers/nvidia/test_safety.py +++ b/tests/unit/providers/nvidia/test_safety.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import json import os import unittest from typing import Any @@ -139,8 +138,8 @@ class TestNVIDIASafetyAdapter(unittest.TestCase): data={ "model": shield_id, "messages": [ - json.loads(messages[0].model_dump_json()), - json.loads(messages[1].model_dump_json()), + {"role": "user", "content": "Hello, how are you?"}, + {"role": "assistant", "content": "I'm doing well, thank you for asking!"}, ], "temperature": 1.0, "top_p": 1, @@ -193,8 +192,8 @@ class TestNVIDIASafetyAdapter(unittest.TestCase): data={ "model": shield_id, "messages": [ - json.loads(messages[0].model_dump_json()), - json.loads(messages[1].model_dump_json()), + {"role": "user", "content": "Hello, how are you?"}, + {"role": "assistant", "content": "I'm doing well, thank you for asking!"}, ], "temperature": 1.0, "top_p": 1, @@ -269,8 +268,8 @@ class TestNVIDIASafetyAdapter(unittest.TestCase): data={ "model": shield_id, "messages": [ - json.loads(messages[0].model_dump_json()), - json.loads(messages[1].model_dump_json()), + {"role": "user", "content": "Hello, how are you?"}, + {"role": "assistant", "content": "I'm doing well, thank you for asking!"}, ], "temperature": 1.0, "top_p": 1, diff --git a/tests/unit/providers/nvidia/test_supervised_fine_tuning.py b/tests/unit/providers/nvidia/test_supervised_fine_tuning.py index 7ce89144b..319011be3 100644 --- a/tests/unit/providers/nvidia/test_supervised_fine_tuning.py +++ b/tests/unit/providers/nvidia/test_supervised_fine_tuning.py @@ -10,13 +10,19 @@ import warnings from unittest.mock import patch import pytest -from llama_stack_client.types.algorithm_config_param import LoraFinetuningConfig, QatFinetuningConfig -from llama_stack_client.types.post_training_supervised_fine_tune_params import ( - TrainingConfig, - TrainingConfigDataConfig, - TrainingConfigOptimizerConfig, -) +from llama_stack.apis.models import Model, ModelType +from llama_stack.apis.post_training.post_training import ( + DataConfig, + DatasetFormat, + LoraFinetuningConfig, + OptimizerConfig, + OptimizerType, + QATFinetuningConfig, + TrainingConfig, +) +from llama_stack.distribution.library_client import convert_pydantic_to_json_value +from llama_stack.providers.remote.inference.nvidia.nvidia import NVIDIAConfig, NVIDIAInferenceAdapter from llama_stack.providers.remote.post_training.nvidia.post_training import ( ListNvidiaPostTrainingJobs, NvidiaPostTrainingAdapter, @@ -40,8 +46,22 @@ class TestNvidiaPostTraining(unittest.TestCase): ) self.mock_make_request = self.make_request_patcher.start() + # Mock the inference client + inference_config = NVIDIAConfig(base_url=os.environ["NVIDIA_BASE_URL"], api_key=None) + self.inference_adapter = NVIDIAInferenceAdapter(inference_config) + + self.mock_client = unittest.mock.MagicMock() + self.mock_client.chat.completions.create = unittest.mock.AsyncMock() + self.inference_mock_make_request = self.mock_client.chat.completions.create + self.inference_make_request_patcher = patch( + "llama_stack.providers.remote.inference.nvidia.nvidia.NVIDIAInferenceAdapter._get_client", + return_value=self.mock_client, + ) + self.inference_make_request_patcher.start() + def tearDown(self): self.make_request_patcher.stop() + self.inference_make_request_patcher.stop() @pytest.fixture(autouse=True) def inject_fixtures(self, run_async): @@ -105,7 +125,7 @@ class TestNvidiaPostTraining(unittest.TestCase): "batch_size": 16, "epochs": 2, "learning_rate": 0.0001, - "lora": {"adapter_dim": 16, "adapter_dropout": 0.1}, + "lora": {"alpha": 16}, }, "output_model": "default/job-1234", "status": "created", @@ -116,8 +136,6 @@ class TestNvidiaPostTraining(unittest.TestCase): algorithm_config = LoraFinetuningConfig( type="LoRA", - adapter_dim=16, - adapter_dropout=0.1, apply_lora_to_mlp=True, apply_lora_to_output=True, alpha=16, @@ -125,10 +143,15 @@ class TestNvidiaPostTraining(unittest.TestCase): lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"], ) - data_config = TrainingConfigDataConfig(dataset_id="sample-basic-test", batch_size=16) + data_config = DataConfig( + dataset_id="sample-basic-test", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct + ) - optimizer_config = TrainingConfigOptimizerConfig( + optimizer_config = OptimizerConfig( + optimizer_type=OptimizerType.adam, lr=0.0001, + weight_decay=0.01, + num_warmup_steps=100, ) training_config = TrainingConfig( @@ -145,7 +168,7 @@ class TestNvidiaPostTraining(unittest.TestCase): model="meta-llama/Llama-3.1-8B-Instruct", checkpoint_dir="", algorithm_config=algorithm_config, - training_config=training_config, + training_config=convert_pydantic_to_json_value(training_config), logger_config={}, hyperparam_search_config={}, ) @@ -169,16 +192,22 @@ class TestNvidiaPostTraining(unittest.TestCase): "epochs": 2, "batch_size": 16, "learning_rate": 0.0001, - "lora": {"alpha": 16, "adapter_dim": 16, "adapter_dropout": 0.1}, + "weight_decay": 0.01, + "lora": {"alpha": 16}, }, }, ) def test_supervised_fine_tune_with_qat(self): - algorithm_config = QatFinetuningConfig(type="QAT", quantizer_name="quantizer_name", group_size=1) - data_config = TrainingConfigDataConfig(dataset_id="sample-basic-test", batch_size=16) - optimizer_config = TrainingConfigOptimizerConfig( + algorithm_config = QATFinetuningConfig(type="QAT", quantizer_name="quantizer_name", group_size=1) + data_config = DataConfig( + dataset_id="sample-basic-test", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct + ) + optimizer_config = OptimizerConfig( + optimizer_type=OptimizerType.adam, lr=0.0001, + weight_decay=0.01, + num_warmup_steps=100, ) training_config = TrainingConfig( n_epochs=2, @@ -193,42 +222,55 @@ class TestNvidiaPostTraining(unittest.TestCase): model="meta-llama/Llama-3.1-8B-Instruct", checkpoint_dir="", algorithm_config=algorithm_config, - training_config=training_config, + training_config=convert_pydantic_to_json_value(training_config), logger_config={}, hyperparam_search_config={}, ) ) def test_get_training_job_status(self): - self.mock_make_request.return_value = { - "created_at": "2024-12-09T04:06:28.580220", - "updated_at": "2024-12-09T04:21:19.852832", - "status": "completed", - "steps_completed": 1210, - "epochs_completed": 2, - "percentage_done": 100.0, - "best_epoch": 2, - "train_loss": 1.718016266822815, - "val_loss": 1.8661999702453613, - } + customizer_status_to_job_status = [ + ("running", "in_progress"), + ("completed", "completed"), + ("failed", "failed"), + ("cancelled", "cancelled"), + ("pending", "scheduled"), + ("unknown", "scheduled"), + ] - job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2" + for customizer_status, expected_status in customizer_status_to_job_status: + with self.subTest(customizer_status=customizer_status, expected_status=expected_status): + self.mock_make_request.return_value = { + "created_at": "2024-12-09T04:06:28.580220", + "updated_at": "2024-12-09T04:21:19.852832", + "status": customizer_status, + "steps_completed": 1210, + "epochs_completed": 2, + "percentage_done": 100.0, + "best_epoch": 2, + "train_loss": 1.718016266822815, + "val_loss": 1.8661999702453613, + } - status = self.run_async(self.adapter.get_training_job_status(job_uuid=job_id)) + job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2" - assert isinstance(status, NvidiaPostTrainingJobStatusResponse) - assert status.status.value == "completed" - assert status.steps_completed == 1210 - assert status.epochs_completed == 2 - assert status.percentage_done == 100.0 - assert status.best_epoch == 2 - assert status.train_loss == 1.718016266822815 - assert status.val_loss == 1.8661999702453613 + status = self.run_async(self.adapter.get_training_job_status(job_uuid=job_id)) - self.mock_make_request.assert_called_once() - self._assert_request( - self.mock_make_request, "GET", f"/v1/customization/jobs/{job_id}/status", expected_params={"job_id": job_id} - ) + assert isinstance(status, NvidiaPostTrainingJobStatusResponse) + assert status.status.value == expected_status + assert status.steps_completed == 1210 + assert status.epochs_completed == 2 + assert status.percentage_done == 100.0 + assert status.best_epoch == 2 + assert status.train_loss == 1.718016266822815 + assert status.val_loss == 1.8661999702453613 + + self._assert_request( + self.mock_make_request, + "GET", + f"/v1/customization/jobs/{job_id}/status", + expected_params={"job_id": job_id}, + ) def test_get_training_jobs(self): job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2" @@ -290,6 +332,31 @@ class TestNvidiaPostTraining(unittest.TestCase): expected_params={"job_id": job_id}, ) + def test_inference_register_model(self): + model_id = "default/job-1234" + model_type = ModelType.llm + model = Model( + identifier=model_id, + provider_id="nvidia", + provider_model_id=model_id, + provider_resource_id=model_id, + model_type=model_type, + ) + result = self.run_async(self.inference_adapter.register_model(model)) + assert result == model + assert len(self.inference_adapter.alias_to_provider_id_map) > 1 + assert self.inference_adapter.get_provider_model_id(model.provider_model_id) == model_id + + with patch.object(self.inference_adapter, "chat_completion") as mock_chat_completion: + self.run_async( + self.inference_adapter.chat_completion( + model_id=model_id, + messages=[{"role": "user", "content": "Hello, model"}], + ) + ) + + mock_chat_completion.assert_called() + if __name__ == "__main__": unittest.main() diff --git a/tests/unit/providers/utils/inference/test_openai_compat.py b/tests/unit/providers/utils/inference/test_openai_compat.py new file mode 100644 index 000000000..4c75b8a2f --- /dev/null +++ b/tests/unit/providers/utils/inference/test_openai_compat.py @@ -0,0 +1,116 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import pytest + +from llama_stack.apis.common.content_types import TextContentItem +from llama_stack.apis.inference.inference import ( + CompletionMessage, + OpenAIAssistantMessageParam, + OpenAIChatCompletionContentPartTextParam, + OpenAISystemMessageParam, + OpenAIUserMessageParam, + SystemMessage, + UserMessage, +) +from llama_stack.models.llama.datatypes import BuiltinTool, StopReason, ToolCall +from llama_stack.providers.utils.inference.openai_compat import ( + convert_message_to_openai_dict, + openai_messages_to_messages, +) + + +@pytest.mark.asyncio +async def test_convert_message_to_openai_dict(): + message = UserMessage(content=[TextContentItem(text="Hello, world!")], role="user") + assert await convert_message_to_openai_dict(message) == { + "role": "user", + "content": [{"type": "text", "text": "Hello, world!"}], + } + + +# Test convert_message_to_openai_dict with a tool call +@pytest.mark.asyncio +async def test_convert_message_to_openai_dict_with_tool_call(): + message = CompletionMessage( + content="", + tool_calls=[ + ToolCall(call_id="123", tool_name="test_tool", arguments_json='{"foo": "bar"}', arguments={"foo": "bar"}) + ], + stop_reason=StopReason.end_of_turn, + ) + + openai_dict = await convert_message_to_openai_dict(message) + + assert openai_dict == { + "role": "assistant", + "content": [{"type": "text", "text": ""}], + "tool_calls": [ + {"id": "123", "type": "function", "function": {"name": "test_tool", "arguments": '{"foo": "bar"}'}} + ], + } + + +@pytest.mark.asyncio +async def test_convert_message_to_openai_dict_with_builtin_tool_call(): + message = CompletionMessage( + content="", + tool_calls=[ + ToolCall( + call_id="123", + tool_name=BuiltinTool.brave_search, + arguments_json='{"foo": "bar"}', + arguments={"foo": "bar"}, + ) + ], + stop_reason=StopReason.end_of_turn, + ) + + openai_dict = await convert_message_to_openai_dict(message) + + assert openai_dict == { + "role": "assistant", + "content": [{"type": "text", "text": ""}], + "tool_calls": [ + {"id": "123", "type": "function", "function": {"name": "brave_search", "arguments": '{"foo": "bar"}'}} + ], + } + + +@pytest.mark.asyncio +async def test_openai_messages_to_messages_with_content_str(): + openai_messages = [ + OpenAISystemMessageParam(content="system message"), + OpenAIUserMessageParam(content="user message"), + OpenAIAssistantMessageParam(content="assistant message"), + ] + + llama_messages = openai_messages_to_messages(openai_messages) + assert len(llama_messages) == 3 + assert isinstance(llama_messages[0], SystemMessage) + assert isinstance(llama_messages[1], UserMessage) + assert isinstance(llama_messages[2], CompletionMessage) + assert llama_messages[0].content == "system message" + assert llama_messages[1].content == "user message" + assert llama_messages[2].content == "assistant message" + + +@pytest.mark.asyncio +async def test_openai_messages_to_messages_with_content_list(): + openai_messages = [ + OpenAISystemMessageParam(content=[OpenAIChatCompletionContentPartTextParam(text="system message")]), + OpenAIUserMessageParam(content=[OpenAIChatCompletionContentPartTextParam(text="user message")]), + OpenAIAssistantMessageParam(content=[OpenAIChatCompletionContentPartTextParam(text="assistant message")]), + ] + + llama_messages = openai_messages_to_messages(openai_messages) + assert len(llama_messages) == 3 + assert isinstance(llama_messages[0], SystemMessage) + assert isinstance(llama_messages[1], UserMessage) + assert isinstance(llama_messages[2], CompletionMessage) + assert llama_messages[0].content[0].text == "system message" + assert llama_messages[1].content[0].text == "user message" + assert llama_messages[2].content[0].text == "assistant message" diff --git a/tests/unit/providers/utils/test_model_registry.py b/tests/unit/providers/utils/test_model_registry.py new file mode 100644 index 000000000..67f8a138f --- /dev/null +++ b/tests/unit/providers/utils/test_model_registry.py @@ -0,0 +1,163 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +# +# ModelRegistryHelper provides mixin functionality for registering and +# unregistering models. It maintains a mapping of model ID / aliases to +# provider model IDs. +# +# Test cases - +# - Looking up an alias that does not exist should return None. +# - Registering a model + provider ID should add the model to the registry. If +# provider ID is known or an alias for a provider ID. +# - Registering an existing model should return an error. Unless it's a +# dulicate entry. +# - Unregistering a model should remove it from the registry. +# - Unregistering a model that does not exist should return an error. +# - Supported model ID and their aliases are registered during initialization. +# Only aliases are added afterwards. +# +# Questions - +# - Should we be allowed to register models w/o provider model IDs? No. +# According to POST /v1/models, required params are +# - identifier +# - provider_resource_id +# - provider_id +# - type +# - metadata +# - model_type +# +# TODO: llama_model functionality +# + +import pytest + +from llama_stack.apis.models.models import Model +from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper, ProviderModelEntry + + +@pytest.fixture +def known_model() -> Model: + return Model( + provider_id="provider", + identifier="known-model", + provider_resource_id="known-provider-id", + ) + + +@pytest.fixture +def known_model2() -> Model: + return Model( + provider_id="provider", + identifier="known-model2", + provider_resource_id="known-provider-id2", + ) + + +@pytest.fixture +def known_provider_model(known_model: Model) -> ProviderModelEntry: + return ProviderModelEntry( + provider_model_id=known_model.provider_resource_id, + aliases=[known_model.model_id], + ) + + +@pytest.fixture +def known_provider_model2(known_model2: Model) -> ProviderModelEntry: + return ProviderModelEntry( + provider_model_id=known_model2.provider_resource_id, + # aliases=[], + ) + + +@pytest.fixture +def unknown_model() -> Model: + return Model( + provider_id="provider", + identifier="unknown-model", + provider_resource_id="unknown-provider-id", + ) + + +@pytest.fixture +def helper(known_provider_model: ProviderModelEntry, known_provider_model2: ProviderModelEntry) -> ModelRegistryHelper: + return ModelRegistryHelper([known_provider_model, known_provider_model2]) + + +@pytest.mark.asyncio +async def test_lookup_unknown_model(helper: ModelRegistryHelper, unknown_model: Model) -> None: + assert helper.get_provider_model_id(unknown_model.model_id) is None + + +@pytest.mark.asyncio +async def test_register_unknown_provider_model(helper: ModelRegistryHelper, unknown_model: Model) -> None: + with pytest.raises(ValueError): + await helper.register_model(unknown_model) + + +@pytest.mark.asyncio +async def test_register_model(helper: ModelRegistryHelper, known_model: Model) -> None: + model = Model( + provider_id=known_model.provider_id, + identifier="new-model", + provider_resource_id=known_model.provider_resource_id, + ) + assert helper.get_provider_model_id(model.model_id) is None + await helper.register_model(model) + assert helper.get_provider_model_id(model.model_id) == model.provider_resource_id + + +@pytest.mark.asyncio +async def test_register_model_from_alias(helper: ModelRegistryHelper, known_model: Model) -> None: + model = Model( + provider_id=known_model.provider_id, + identifier="new-model", + provider_resource_id=known_model.model_id, # use known model's id as an alias for the supported model id + ) + assert helper.get_provider_model_id(model.model_id) is None + await helper.register_model(model) + assert helper.get_provider_model_id(model.model_id) == known_model.provider_resource_id + + +@pytest.mark.asyncio +async def test_register_model_existing(helper: ModelRegistryHelper, known_model: Model) -> None: + await helper.register_model(known_model) + assert helper.get_provider_model_id(known_model.model_id) == known_model.provider_resource_id + + +@pytest.mark.asyncio +async def test_register_model_existing_different( + helper: ModelRegistryHelper, known_model: Model, known_model2: Model +) -> None: + known_model.provider_resource_id = known_model2.provider_resource_id + with pytest.raises(ValueError): + await helper.register_model(known_model) + + +@pytest.mark.asyncio +async def test_unregister_model(helper: ModelRegistryHelper, known_model: Model) -> None: + await helper.register_model(known_model) # duplicate entry + assert helper.get_provider_model_id(known_model.model_id) == known_model.provider_model_id + await helper.unregister_model(known_model.model_id) + assert helper.get_provider_model_id(known_model.model_id) is None + + +@pytest.mark.asyncio +async def test_unregister_unknown_model(helper: ModelRegistryHelper, unknown_model: Model) -> None: + with pytest.raises(ValueError): + await helper.unregister_model(unknown_model.model_id) + + +@pytest.mark.asyncio +async def test_register_model_during_init(helper: ModelRegistryHelper, known_model: Model) -> None: + assert helper.get_provider_model_id(known_model.provider_resource_id) == known_model.provider_model_id + + +@pytest.mark.asyncio +async def test_unregister_model_during_init(helper: ModelRegistryHelper, known_model: Model) -> None: + assert helper.get_provider_model_id(known_model.provider_resource_id) == known_model.provider_model_id + await helper.unregister_model(known_model.provider_resource_id) + assert helper.get_provider_model_id(known_model.provider_resource_id) is None diff --git a/tests/unit/registry/test_registry_acl.py b/tests/unit/registry/test_registry_acl.py index ee8f28176..2a50b2840 100644 --- a/tests/unit/registry/test_registry_acl.py +++ b/tests/unit/registry/test_registry_acl.py @@ -12,7 +12,7 @@ import pytest from llama_stack.apis.models import ModelType from llama_stack.distribution.datatypes import ModelWithACL -from llama_stack.distribution.server.auth import AccessAttributes +from llama_stack.distribution.server.auth_providers import AccessAttributes from llama_stack.distribution.store.registry import CachedDiskDistributionRegistry from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig from llama_stack.providers.utils.kvstore.sqlite import SqliteKVStoreImpl diff --git a/tests/unit/server/test_access_control.py b/tests/unit/server/test_access_control.py index ab0feb1a9..7d92a5cf5 100644 --- a/tests/unit/server/test_access_control.py +++ b/tests/unit/server/test_access_control.py @@ -22,7 +22,7 @@ from llama_stack.providers.utils.kvstore.sqlite import SqliteKVStoreImpl class AsyncMock(MagicMock): async def __call__(self, *args, **kwargs): - return super(AsyncMock, self).__call__(*args, **kwargs) + return super().__call__(*args, **kwargs) def _return_model(model): diff --git a/tests/unit/server/test_auth.py b/tests/unit/server/test_auth.py index 5e93719d2..a75f9e6b6 100644 --- a/tests/unit/server/test_auth.py +++ b/tests/unit/server/test_auth.py @@ -10,7 +10,9 @@ import pytest from fastapi import FastAPI from fastapi.testclient import TestClient +from llama_stack.distribution.datatypes import AccessAttributes from llama_stack.distribution.server.auth import AuthenticationMiddleware +from llama_stack.distribution.server.auth_providers import AuthProviderConfig, AuthProviderType class MockResponse: @@ -38,9 +40,23 @@ def invalid_api_key(): @pytest.fixture -def app(mock_auth_endpoint): +def valid_token(): + return "valid.jwt.token" + + +@pytest.fixture +def invalid_token(): + return "invalid.jwt.token" + + +@pytest.fixture +def http_app(mock_auth_endpoint): app = FastAPI() - app.add_middleware(AuthenticationMiddleware, auth_endpoint=mock_auth_endpoint) + auth_config = AuthProviderConfig( + provider_type=AuthProviderType.CUSTOM, + config={"endpoint": mock_auth_endpoint}, + ) + app.add_middleware(AuthenticationMiddleware, auth_config=auth_config) @app.get("/test") def test_endpoint(): @@ -50,8 +66,29 @@ def app(mock_auth_endpoint): @pytest.fixture -def client(app): - return TestClient(app) +def k8s_app(): + app = FastAPI() + auth_config = AuthProviderConfig( + provider_type=AuthProviderType.KUBERNETES, + config={"api_server_url": "https://kubernetes.default.svc"}, + ) + app.add_middleware(AuthenticationMiddleware, auth_config=auth_config) + + @app.get("/test") + def test_endpoint(): + return {"message": "Authentication successful"} + + return app + + +@pytest.fixture +def http_client(http_app): + return TestClient(http_app) + + +@pytest.fixture +def k8s_client(k8s_app): + return TestClient(k8s_app) @pytest.fixture @@ -61,7 +98,7 @@ def mock_scope(): "path": "/models/list", "headers": [ (b"content-type", b"application/json"), - (b"authorization", b"Bearer test-api-key"), + (b"authorization", b"Bearer test.jwt.token"), (b"user-agent", b"test-user-agent"), ], "query_string": b"limit=100&offset=0", @@ -69,13 +106,38 @@ def mock_scope(): @pytest.fixture -def mock_middleware(mock_auth_endpoint): +def mock_http_middleware(mock_auth_endpoint): mock_app = AsyncMock() - return AuthenticationMiddleware(mock_app, mock_auth_endpoint), mock_app + auth_config = AuthProviderConfig( + provider_type=AuthProviderType.CUSTOM, + config={"endpoint": mock_auth_endpoint}, + ) + return AuthenticationMiddleware(mock_app, auth_config), mock_app + + +@pytest.fixture +def mock_k8s_middleware(): + mock_app = AsyncMock() + auth_config = AuthProviderConfig( + provider_type=AuthProviderType.KUBERNETES, + config={"api_server_url": "https://kubernetes.default.svc"}, + ) + return AuthenticationMiddleware(mock_app, auth_config), mock_app async def mock_post_success(*args, **kwargs): - return MockResponse(200, {"message": "Authentication successful"}) + return MockResponse( + 200, + { + "message": "Authentication successful", + "access_attributes": { + "roles": ["admin", "user"], + "teams": ["ml-team", "nlp-team"], + "projects": ["llama-3", "project-x"], + "namespaces": ["research", "production"], + }, + }, + ) async def mock_post_failure(*args, **kwargs): @@ -86,45 +148,46 @@ async def mock_post_exception(*args, **kwargs): raise Exception("Connection error") -def test_missing_auth_header(client): - response = client.get("/test") +# HTTP Endpoint Tests +def test_missing_auth_header(http_client): + response = http_client.get("/test") assert response.status_code == 401 assert "Missing or invalid Authorization header" in response.json()["error"]["message"] -def test_invalid_auth_header_format(client): - response = client.get("/test", headers={"Authorization": "InvalidFormat token123"}) +def test_invalid_auth_header_format(http_client): + response = http_client.get("/test", headers={"Authorization": "InvalidFormat token123"}) assert response.status_code == 401 assert "Missing or invalid Authorization header" in response.json()["error"]["message"] @patch("httpx.AsyncClient.post", new=mock_post_success) -def test_valid_authentication(client, valid_api_key): - response = client.get("/test", headers={"Authorization": f"Bearer {valid_api_key}"}) +def test_valid_http_authentication(http_client, valid_api_key): + response = http_client.get("/test", headers={"Authorization": f"Bearer {valid_api_key}"}) assert response.status_code == 200 assert response.json() == {"message": "Authentication successful"} @patch("httpx.AsyncClient.post", new=mock_post_failure) -def test_invalid_authentication(client, invalid_api_key): - response = client.get("/test", headers={"Authorization": f"Bearer {invalid_api_key}"}) +def test_invalid_http_authentication(http_client, invalid_api_key): + response = http_client.get("/test", headers={"Authorization": f"Bearer {invalid_api_key}"}) assert response.status_code == 401 assert "Authentication failed" in response.json()["error"]["message"] @patch("httpx.AsyncClient.post", new=mock_post_exception) -def test_auth_service_error(client, valid_api_key): - response = client.get("/test", headers={"Authorization": f"Bearer {valid_api_key}"}) +def test_http_auth_service_error(http_client, valid_api_key): + response = http_client.get("/test", headers={"Authorization": f"Bearer {valid_api_key}"}) assert response.status_code == 401 assert "Authentication service error" in response.json()["error"]["message"] -def test_auth_request_payload(client, valid_api_key, mock_auth_endpoint): +def test_http_auth_request_payload(http_client, valid_api_key, mock_auth_endpoint): with patch("httpx.AsyncClient.post") as mock_post: mock_response = MockResponse(200, {"message": "Authentication successful"}) mock_post.return_value = mock_response - client.get( + http_client.get( "/test?param1=value1¶m2=value2", headers={ "Authorization": f"Bearer {valid_api_key}", @@ -149,40 +212,43 @@ def test_auth_request_payload(client, valid_api_key, mock_auth_endpoint): @pytest.mark.asyncio -async def test_auth_middleware_with_access_attributes(mock_middleware, mock_scope): - middleware, mock_app = mock_middleware +async def test_http_middleware_with_access_attributes(mock_http_middleware, mock_scope): + """Test HTTP middleware behavior with access attributes""" + middleware, mock_app = mock_http_middleware mock_receive = AsyncMock() mock_send = AsyncMock() - with patch("httpx.AsyncClient") as mock_client: - mock_client_instance = AsyncMock() - mock_client.return_value.__aenter__.return_value = mock_client_instance - - mock_client_instance.post.return_value = MockResponse( + with patch("httpx.AsyncClient.post") as mock_post: + mock_response = MockResponse( 200, { + "message": "Authentication successful", "access_attributes": { "roles": ["admin", "user"], - "teams": ["ml-team"], - "projects": ["project-x", "project-y"], - } + "teams": ["ml-team", "nlp-team"], + "projects": ["llama-3", "project-x"], + "namespaces": ["research", "production"], + }, }, ) + mock_post.return_value = mock_response await middleware(mock_scope, mock_receive, mock_send) assert "user_attributes" in mock_scope - assert mock_scope["user_attributes"]["roles"] == ["admin", "user"] - assert mock_scope["user_attributes"]["teams"] == ["ml-team"] - assert mock_scope["user_attributes"]["projects"] == ["project-x", "project-y"] + attributes = mock_scope["user_attributes"] + assert attributes["roles"] == ["admin", "user"] + assert attributes["teams"] == ["ml-team", "nlp-team"] + assert attributes["projects"] == ["llama-3", "project-x"] + assert attributes["namespaces"] == ["research", "production"] mock_app.assert_called_once_with(mock_scope, mock_receive, mock_send) @pytest.mark.asyncio -async def test_auth_middleware_no_attributes(mock_middleware, mock_scope): +async def test_http_middleware_no_attributes(mock_http_middleware, mock_scope): """Test middleware behavior with no access attributes""" - middleware, mock_app = mock_middleware + middleware, mock_app = mock_http_middleware mock_receive = AsyncMock() mock_send = AsyncMock() @@ -203,4 +269,104 @@ async def test_auth_middleware_no_attributes(mock_middleware, mock_scope): assert "user_attributes" in mock_scope attributes = mock_scope["user_attributes"] assert "namespaces" in attributes - assert attributes["namespaces"] == ["test-api-key"] + assert attributes["namespaces"] == ["test.jwt.token"] + + +# Kubernetes Tests +def test_missing_auth_header_k8s(k8s_client): + response = k8s_client.get("/test") + assert response.status_code == 401 + assert "Missing or invalid Authorization header" in response.json()["error"]["message"] + + +def test_invalid_auth_header_format_k8s(k8s_client): + response = k8s_client.get("/test", headers={"Authorization": "InvalidFormat token123"}) + assert response.status_code == 401 + assert "Missing or invalid Authorization header" in response.json()["error"]["message"] + + +@patch("kubernetes.client.ApiClient") +def test_valid_k8s_authentication(mock_api_client, k8s_client, valid_token): + # Mock the Kubernetes client + mock_client = AsyncMock() + mock_api_client.return_value = mock_client + + # Mock successful token validation + mock_client.set_default_header = AsyncMock() + + # Mock the token validation to return valid access attributes + with patch("llama_stack.distribution.server.auth_providers.KubernetesAuthProvider.validate_token") as mock_validate: + mock_validate.return_value = AccessAttributes( + roles=["admin"], teams=["ml-team"], projects=["llama-3"], namespaces=["research"] + ) + response = k8s_client.get("/test", headers={"Authorization": f"Bearer {valid_token}"}) + assert response.status_code == 200 + assert response.json() == {"message": "Authentication successful"} + + +@patch("kubernetes.client.ApiClient") +def test_invalid_k8s_authentication(mock_api_client, k8s_client, invalid_token): + # Mock the Kubernetes client + mock_client = AsyncMock() + mock_api_client.return_value = mock_client + + # Mock failed token validation by raising an exception + with patch("llama_stack.distribution.server.auth_providers.KubernetesAuthProvider.validate_token") as mock_validate: + mock_validate.side_effect = ValueError("Invalid or expired token") + response = k8s_client.get("/test", headers={"Authorization": f"Bearer {invalid_token}"}) + assert response.status_code == 401 + assert "Invalid or expired token" in response.json()["error"]["message"] + + +@pytest.mark.asyncio +async def test_k8s_middleware_with_access_attributes(mock_k8s_middleware, mock_scope): + middleware, mock_app = mock_k8s_middleware + mock_receive = AsyncMock() + mock_send = AsyncMock() + + with patch("kubernetes.client.ApiClient") as mock_api_client: + mock_client = AsyncMock() + mock_api_client.return_value = mock_client + + # Mock successful token validation + mock_client.set_default_header = AsyncMock() + + # Mock token payload with access attributes + mock_token_parts = ["header", "eyJzdWIiOiJhZG1pbiIsImdyb3VwcyI6WyJtbC10ZWFtIl19", "signature"] + mock_scope["headers"][1] = (b"authorization", f"Bearer {'.'.join(mock_token_parts)}".encode()) + + await middleware(mock_scope, mock_receive, mock_send) + + assert "user_attributes" in mock_scope + assert mock_scope["user_attributes"]["roles"] == ["admin"] + assert mock_scope["user_attributes"]["teams"] == ["ml-team"] + + mock_app.assert_called_once_with(mock_scope, mock_receive, mock_send) + + +@pytest.mark.asyncio +async def test_k8s_middleware_no_attributes(mock_k8s_middleware, mock_scope): + """Test middleware behavior with no access attributes""" + middleware, mock_app = mock_k8s_middleware + mock_receive = AsyncMock() + mock_send = AsyncMock() + + with patch("kubernetes.client.ApiClient") as mock_api_client: + mock_client = AsyncMock() + mock_api_client.return_value = mock_client + + # Mock successful token validation + mock_client.set_default_header = AsyncMock() + + # Mock token payload without access attributes + mock_token_parts = ["header", "eyJzdWIiOiJhZG1pbiJ9", "signature"] + mock_scope["headers"][1] = (b"authorization", f"Bearer {'.'.join(mock_token_parts)}".encode()) + + await middleware(mock_scope, mock_receive, mock_send) + + assert "user_attributes" in mock_scope + attributes = mock_scope["user_attributes"] + assert "roles" in attributes + assert attributes["roles"] == ["admin"] + + mock_app.assert_called_once_with(mock_scope, mock_receive, mock_send) diff --git a/tests/unit/server/test_resolver.py b/tests/unit/server/test_resolver.py index fcf0b3945..3af9535a0 100644 --- a/tests/unit/server/test_resolver.py +++ b/tests/unit/server/test_resolver.py @@ -6,7 +6,7 @@ import inspect import sys -from typing import Any, Dict, Protocol +from typing import Any, Protocol from unittest.mock import AsyncMock, MagicMock import pytest @@ -48,14 +48,14 @@ class SampleConfig(BaseModel): ) @classmethod - def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]: + def sample_run_config(cls, **kwargs: Any) -> dict[str, Any]: return { "foo": "baz", } class SampleImpl: - def __init__(self, config: SampleConfig, deps: Dict[Api, Any], provider_spec: ProviderSpec = None): + def __init__(self, config: SampleConfig, deps: dict[Api, Any], provider_spec: ProviderSpec = None): self.__provider_id__ = "test_provider" self.__provider_spec__ = provider_spec self.__provider_config__ = config diff --git a/tests/unit/server/test_sse.py b/tests/unit/server/test_sse.py new file mode 100644 index 000000000..c78122294 --- /dev/null +++ b/tests/unit/server/test_sse.py @@ -0,0 +1,91 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import asyncio + +import pytest + +from llama_stack.distribution.server.server import create_sse_event, sse_generator + + +@pytest.mark.asyncio +async def test_sse_generator_basic(): + # An AsyncIterator wrapped in an Awaitable, just like our web methods + async def async_event_gen(): + async def event_gen(): + yield "Test event 1" + yield "Test event 2" + + return event_gen() + + sse_gen = sse_generator(async_event_gen()) + assert sse_gen is not None + + # Test that the events are streamed correctly + seen_events = [] + async for event in sse_gen: + seen_events.append(event) + assert len(seen_events) == 2 + assert seen_events[0] == create_sse_event("Test event 1") + assert seen_events[1] == create_sse_event("Test event 2") + + +@pytest.mark.asyncio +async def test_sse_generator_client_disconnected(): + # An AsyncIterator wrapped in an Awaitable, just like our web methods + async def async_event_gen(): + async def event_gen(): + yield "Test event 1" + # Simulate a client disconnect before emitting event 2 + raise asyncio.CancelledError() + + return event_gen() + + sse_gen = sse_generator(async_event_gen()) + assert sse_gen is not None + + seen_events = [] + async for event in sse_gen: + seen_events.append(event) + + # We should see 1 event before the client disconnected + assert len(seen_events) == 1 + assert seen_events[0] == create_sse_event("Test event 1") + + +@pytest.mark.asyncio +async def test_sse_generator_client_disconnected_before_response_starts(): + # Disconnect before the response starts + async def async_event_gen(): + raise asyncio.CancelledError() + + sse_gen = sse_generator(async_event_gen()) + assert sse_gen is not None + + seen_events = [] + async for event in sse_gen: + seen_events.append(event) + + # No events should be seen since the client disconnected immediately + assert len(seen_events) == 0 + + +@pytest.mark.asyncio +async def test_sse_generator_error_before_response_starts(): + # Raise an error before the response starts + async def async_event_gen(): + raise Exception("Test error") + + sse_gen = sse_generator(async_event_gen()) + assert sse_gen is not None + + seen_events = [] + async for event in sse_gen: + seen_events.append(event) + + # We should have 1 error event + assert len(seen_events) == 1 + assert 'data: {"error":' in seen_events[0] diff --git a/tests/verifications/README.md b/tests/verifications/README.md index 986ff1087..88762e0ba 100644 --- a/tests/verifications/README.md +++ b/tests/verifications/README.md @@ -8,29 +8,44 @@ This framework allows you to run the same set of verification tests against diff ## Features -The verification suite currently tests: +The verification suite currently tests the following in both streaming and non-streaming modes: -- Basic chat completions (streaming and non-streaming) +- Basic chat completions - Image input capabilities - Structured JSON output formatting - Tool calling functionality +## Report + +The lastest report can be found at [REPORT.md](REPORT.md). + +To update the report, ensure you have the API keys set, +```bash +export OPENAI_API_KEY= +export FIREWORKS_API_KEY= +export TOGETHER_API_KEY= +``` +then run +```bash +uv run --with-editable ".[dev]" python tests/verifications/generate_report.py --run-tests +``` + ## Running Tests To run the verification tests, use pytest with the following parameters: ```bash cd llama-stack -pytest tests/verifications/openai --provider= +pytest tests/verifications/openai_api --provider= ``` Example: ```bash # Run all tests -pytest tests/verifications/openai --provider=together +pytest tests/verifications/openai_api --provider=together # Only run tests with Llama 4 models -pytest tests/verifications/openai --provider=together -k 'Llama-4' +pytest tests/verifications/openai_api --provider=together -k 'Llama-4' ``` ### Parameters @@ -41,23 +56,22 @@ pytest tests/verifications/openai --provider=together -k 'Llama-4' ## Supported Providers -The verification suite currently supports: -- OpenAI -- Fireworks -- Together -- Groq -- Cerebras +The verification suite supports any provider with an OpenAI compatible endpoint. + +See `tests/verifications/conf/` for the list of supported providers. + +To run on a new provider, simply add a new yaml file to the `conf/` directory with the provider config. See `tests/verifications/conf/together.yaml` for an example. ## Adding New Test Cases -To add new test cases, create appropriate JSON files in the `openai/fixtures/test_cases/` directory following the existing patterns. +To add new test cases, create appropriate JSON files in the `openai_api/fixtures/test_cases/` directory following the existing patterns. ## Structure - `__init__.py` - Marks the directory as a Python package -- `conftest.py` - Global pytest configuration and fixtures -- `openai/` - Tests specific to OpenAI-compatible APIs +- `conf/` - Provider-specific configuration files +- `openai_api/` - Tests specific to OpenAI-compatible APIs - `fixtures/` - Test fixtures and utilities - `fixtures.py` - Provider-specific fixtures - `load.py` - Utilities for loading test cases diff --git a/tests/verifications/REPORT.md b/tests/verifications/REPORT.md index 2309c6404..2a700fa9c 100644 --- a/tests/verifications/REPORT.md +++ b/tests/verifications/REPORT.md @@ -1,6 +1,6 @@ # Test Results Report -*Generated on: 2025-04-10 16:48:18* +*Generated on: 2025-04-17 12:42:33* *This report was generated by running `python tests/verifications/generate_report.py`* @@ -15,22 +15,74 @@ | Provider | Pass Rate | Tests Passed | Total Tests | | --- | --- | --- | --- | -| Together | 64.7% | 22 | 34 | -| Fireworks | 82.4% | 28 | 34 | -| Openai | 100.0% | 24 | 24 | +| Meta_reference | 100.0% | 28 | 28 | +| Together | 50.0% | 40 | 80 | +| Fireworks | 50.0% | 40 | 80 | +| Openai | 100.0% | 56 | 56 | +## Meta_reference + +*Tests run on: 2025-04-17 12:37:11* + +```bash +# Run all tests for this provider: +pytest tests/verifications/openai_api/test_chat_completion.py --provider=meta_reference -v + +# Example: Run only the 'stream=False' case of test_chat_multi_turn_multiple_images: +pytest tests/verifications/openai_api/test_chat_completion.py --provider=meta_reference -k "test_chat_multi_turn_multiple_images and stream=False" +``` + + +**Model Key (Meta_reference)** + +| Display Name | Full Model ID | +| --- | --- | +| Llama-4-Scout-Instruct | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | + + +| Test | Llama-4-Scout-Instruct | +| --- | --- | +| test_chat_multi_turn_multiple_images (stream=False) | ✅ | +| test_chat_multi_turn_multiple_images (stream=True) | ✅ | +| test_chat_non_streaming_basic (earth) | ✅ | +| test_chat_non_streaming_basic (saturn) | ✅ | +| test_chat_non_streaming_image | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (add_product_tool) | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (compare_monthly_expense_tool) | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (text_then_weather_tool) | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (weather_tool_then_text) | ✅ | +| test_chat_non_streaming_structured_output (calendar) | ✅ | +| test_chat_non_streaming_structured_output (math) | ✅ | +| test_chat_non_streaming_tool_calling | ✅ | +| test_chat_non_streaming_tool_choice_none | ✅ | +| test_chat_non_streaming_tool_choice_required | ✅ | +| test_chat_streaming_basic (earth) | ✅ | +| test_chat_streaming_basic (saturn) | ✅ | +| test_chat_streaming_image | ✅ | +| test_chat_streaming_multi_turn_tool_calling (add_product_tool) | ✅ | +| test_chat_streaming_multi_turn_tool_calling (compare_monthly_expense_tool) | ✅ | +| test_chat_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ✅ | +| test_chat_streaming_multi_turn_tool_calling (text_then_weather_tool) | ✅ | +| test_chat_streaming_multi_turn_tool_calling (weather_tool_then_text) | ✅ | +| test_chat_streaming_structured_output (calendar) | ✅ | +| test_chat_streaming_structured_output (math) | ✅ | +| test_chat_streaming_tool_calling | ✅ | +| test_chat_streaming_tool_choice_none | ✅ | +| test_chat_streaming_tool_choice_required | ✅ | + ## Together -*Tests run on: 2025-04-10 16:46:35* +*Tests run on: 2025-04-17 12:27:45* ```bash # Run all tests for this provider: pytest tests/verifications/openai_api/test_chat_completion.py --provider=together -v -# Example: Run only the 'earth' case of test_chat_non_streaming_basic: -pytest tests/verifications/openai_api/test_chat_completion.py --provider=together -k "test_chat_non_streaming_basic and earth" +# Example: Run only the 'stream=False' case of test_chat_multi_turn_multiple_images: +pytest tests/verifications/openai_api/test_chat_completion.py --provider=together -k "test_chat_multi_turn_multiple_images and stream=False" ``` @@ -45,29 +97,45 @@ pytest tests/verifications/openai_api/test_chat_completion.py --provider=togethe | Test | Llama-3.3-70B-Instruct | Llama-4-Maverick-Instruct | Llama-4-Scout-Instruct | | --- | --- | --- | --- | +| test_chat_multi_turn_multiple_images (stream=False) | ⚪ | ✅ | ✅ | +| test_chat_multi_turn_multiple_images (stream=True) | ⚪ | ❌ | ❌ | | test_chat_non_streaming_basic (earth) | ✅ | ✅ | ✅ | | test_chat_non_streaming_basic (saturn) | ✅ | ✅ | ✅ | | test_chat_non_streaming_image | ⚪ | ✅ | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (add_product_tool) | ✅ | ✅ | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (compare_monthly_expense_tool) | ✅ | ✅ | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ✅ | ❌ | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (text_then_weather_tool) | ❌ | ❌ | ❌ | +| test_chat_non_streaming_multi_turn_tool_calling (weather_tool_then_text) | ✅ | ✅ | ✅ | | test_chat_non_streaming_structured_output (calendar) | ✅ | ✅ | ✅ | | test_chat_non_streaming_structured_output (math) | ✅ | ✅ | ✅ | | test_chat_non_streaming_tool_calling | ✅ | ✅ | ✅ | +| test_chat_non_streaming_tool_choice_none | ❌ | ❌ | ❌ | +| test_chat_non_streaming_tool_choice_required | ✅ | ✅ | ✅ | | test_chat_streaming_basic (earth) | ✅ | ❌ | ❌ | | test_chat_streaming_basic (saturn) | ✅ | ❌ | ❌ | | test_chat_streaming_image | ⚪ | ❌ | ❌ | +| test_chat_streaming_multi_turn_tool_calling (add_product_tool) | ✅ | ❌ | ❌ | +| test_chat_streaming_multi_turn_tool_calling (compare_monthly_expense_tool) | ❌ | ❌ | ❌ | +| test_chat_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ❌ | ❌ | ❌ | +| test_chat_streaming_multi_turn_tool_calling (text_then_weather_tool) | ❌ | ❌ | ❌ | +| test_chat_streaming_multi_turn_tool_calling (weather_tool_then_text) | ❌ | ❌ | ❌ | | test_chat_streaming_structured_output (calendar) | ✅ | ❌ | ❌ | | test_chat_streaming_structured_output (math) | ✅ | ❌ | ❌ | | test_chat_streaming_tool_calling | ✅ | ❌ | ❌ | +| test_chat_streaming_tool_choice_none | ❌ | ❌ | ❌ | +| test_chat_streaming_tool_choice_required | ✅ | ❌ | ❌ | ## Fireworks -*Tests run on: 2025-04-10 16:44:44* +*Tests run on: 2025-04-17 12:29:53* ```bash # Run all tests for this provider: pytest tests/verifications/openai_api/test_chat_completion.py --provider=fireworks -v -# Example: Run only the 'earth' case of test_chat_non_streaming_basic: -pytest tests/verifications/openai_api/test_chat_completion.py --provider=fireworks -k "test_chat_non_streaming_basic and earth" +# Example: Run only the 'stream=False' case of test_chat_multi_turn_multiple_images: +pytest tests/verifications/openai_api/test_chat_completion.py --provider=fireworks -k "test_chat_multi_turn_multiple_images and stream=False" ``` @@ -82,29 +150,45 @@ pytest tests/verifications/openai_api/test_chat_completion.py --provider=firewor | Test | Llama-3.3-70B-Instruct | Llama-4-Maverick-Instruct | Llama-4-Scout-Instruct | | --- | --- | --- | --- | +| test_chat_multi_turn_multiple_images (stream=False) | ⚪ | ✅ | ✅ | +| test_chat_multi_turn_multiple_images (stream=True) | ⚪ | ✅ | ✅ | | test_chat_non_streaming_basic (earth) | ✅ | ✅ | ✅ | | test_chat_non_streaming_basic (saturn) | ✅ | ✅ | ✅ | | test_chat_non_streaming_image | ⚪ | ✅ | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (add_product_tool) | ❌ | ❌ | ❌ | +| test_chat_non_streaming_multi_turn_tool_calling (compare_monthly_expense_tool) | ❌ | ❌ | ❌ | +| test_chat_non_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ❌ | ❌ | ❌ | +| test_chat_non_streaming_multi_turn_tool_calling (text_then_weather_tool) | ❌ | ❌ | ❌ | +| test_chat_non_streaming_multi_turn_tool_calling (weather_tool_then_text) | ❌ | ❌ | ❌ | | test_chat_non_streaming_structured_output (calendar) | ✅ | ✅ | ✅ | | test_chat_non_streaming_structured_output (math) | ✅ | ✅ | ✅ | | test_chat_non_streaming_tool_calling | ❌ | ❌ | ❌ | +| test_chat_non_streaming_tool_choice_none | ✅ | ✅ | ✅ | +| test_chat_non_streaming_tool_choice_required | ✅ | ❌ | ❌ | | test_chat_streaming_basic (earth) | ✅ | ✅ | ✅ | | test_chat_streaming_basic (saturn) | ✅ | ✅ | ✅ | | test_chat_streaming_image | ⚪ | ✅ | ✅ | +| test_chat_streaming_multi_turn_tool_calling (add_product_tool) | ❌ | ❌ | ❌ | +| test_chat_streaming_multi_turn_tool_calling (compare_monthly_expense_tool) | ❌ | ❌ | ❌ | +| test_chat_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ❌ | ❌ | ❌ | +| test_chat_streaming_multi_turn_tool_calling (text_then_weather_tool) | ❌ | ❌ | ❌ | +| test_chat_streaming_multi_turn_tool_calling (weather_tool_then_text) | ❌ | ❌ | ❌ | | test_chat_streaming_structured_output (calendar) | ✅ | ✅ | ✅ | | test_chat_streaming_structured_output (math) | ✅ | ✅ | ✅ | | test_chat_streaming_tool_calling | ❌ | ❌ | ❌ | +| test_chat_streaming_tool_choice_none | ✅ | ✅ | ✅ | +| test_chat_streaming_tool_choice_required | ✅ | ❌ | ❌ | ## Openai -*Tests run on: 2025-04-10 16:47:28* +*Tests run on: 2025-04-17 12:34:08* ```bash # Run all tests for this provider: pytest tests/verifications/openai_api/test_chat_completion.py --provider=openai -v -# Example: Run only the 'earth' case of test_chat_non_streaming_basic: -pytest tests/verifications/openai_api/test_chat_completion.py --provider=openai -k "test_chat_non_streaming_basic and earth" +# Example: Run only the 'stream=False' case of test_chat_multi_turn_multiple_images: +pytest tests/verifications/openai_api/test_chat_completion.py --provider=openai -k "test_chat_multi_turn_multiple_images and stream=False" ``` @@ -118,15 +202,31 @@ pytest tests/verifications/openai_api/test_chat_completion.py --provider=openai | Test | gpt-4o | gpt-4o-mini | | --- | --- | --- | +| test_chat_multi_turn_multiple_images (stream=False) | ✅ | ✅ | +| test_chat_multi_turn_multiple_images (stream=True) | ✅ | ✅ | | test_chat_non_streaming_basic (earth) | ✅ | ✅ | | test_chat_non_streaming_basic (saturn) | ✅ | ✅ | | test_chat_non_streaming_image | ✅ | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (add_product_tool) | ✅ | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (compare_monthly_expense_tool) | ✅ | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ✅ | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (text_then_weather_tool) | ✅ | ✅ | +| test_chat_non_streaming_multi_turn_tool_calling (weather_tool_then_text) | ✅ | ✅ | | test_chat_non_streaming_structured_output (calendar) | ✅ | ✅ | | test_chat_non_streaming_structured_output (math) | ✅ | ✅ | | test_chat_non_streaming_tool_calling | ✅ | ✅ | +| test_chat_non_streaming_tool_choice_none | ✅ | ✅ | +| test_chat_non_streaming_tool_choice_required | ✅ | ✅ | | test_chat_streaming_basic (earth) | ✅ | ✅ | | test_chat_streaming_basic (saturn) | ✅ | ✅ | | test_chat_streaming_image | ✅ | ✅ | +| test_chat_streaming_multi_turn_tool_calling (add_product_tool) | ✅ | ✅ | +| test_chat_streaming_multi_turn_tool_calling (compare_monthly_expense_tool) | ✅ | ✅ | +| test_chat_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ✅ | ✅ | +| test_chat_streaming_multi_turn_tool_calling (text_then_weather_tool) | ✅ | ✅ | +| test_chat_streaming_multi_turn_tool_calling (weather_tool_then_text) | ✅ | ✅ | | test_chat_streaming_structured_output (calendar) | ✅ | ✅ | | test_chat_streaming_structured_output (math) | ✅ | ✅ | | test_chat_streaming_tool_calling | ✅ | ✅ | +| test_chat_streaming_tool_choice_none | ✅ | ✅ | +| test_chat_streaming_tool_choice_required | ✅ | ✅ | diff --git a/tests/verifications/conf/cerebras.yaml b/tests/verifications/conf/cerebras.yaml index 5b19b4916..37fc713d6 100644 --- a/tests/verifications/conf/cerebras.yaml +++ b/tests/verifications/conf/cerebras.yaml @@ -8,3 +8,4 @@ test_exclusions: llama-3.3-70b: - test_chat_non_streaming_image - test_chat_streaming_image + - test_chat_multi_turn_multiple_images diff --git a/tests/verifications/conf/fireworks-llama-stack.yaml b/tests/verifications/conf/fireworks-llama-stack.yaml index d91443dd9..dffd7c739 100644 --- a/tests/verifications/conf/fireworks-llama-stack.yaml +++ b/tests/verifications/conf/fireworks-llama-stack.yaml @@ -12,3 +12,6 @@ test_exclusions: fireworks/llama-v3p3-70b-instruct: - test_chat_non_streaming_image - test_chat_streaming_image + - test_chat_multi_turn_multiple_images + - test_response_non_streaming_image + - test_response_non_streaming_multi_turn_image diff --git a/tests/verifications/conf/fireworks.yaml b/tests/verifications/conf/fireworks.yaml index f55b707ba..9bb21f706 100644 --- a/tests/verifications/conf/fireworks.yaml +++ b/tests/verifications/conf/fireworks.yaml @@ -12,3 +12,4 @@ test_exclusions: accounts/fireworks/models/llama-v3p3-70b-instruct: - test_chat_non_streaming_image - test_chat_streaming_image + - test_chat_multi_turn_multiple_images diff --git a/tests/verifications/conf/groq-llama-stack.yaml b/tests/verifications/conf/groq-llama-stack.yaml index fd5e9abec..786b79c24 100644 --- a/tests/verifications/conf/groq-llama-stack.yaml +++ b/tests/verifications/conf/groq-llama-stack.yaml @@ -12,3 +12,6 @@ test_exclusions: groq/llama-3.3-70b-versatile: - test_chat_non_streaming_image - test_chat_streaming_image + - test_chat_multi_turn_multiple_images + - test_response_non_streaming_image + - test_response_non_streaming_multi_turn_image diff --git a/tests/verifications/conf/groq.yaml b/tests/verifications/conf/groq.yaml index 76b1244ae..bc3de58e9 100644 --- a/tests/verifications/conf/groq.yaml +++ b/tests/verifications/conf/groq.yaml @@ -12,3 +12,4 @@ test_exclusions: llama-3.3-70b-versatile: - test_chat_non_streaming_image - test_chat_streaming_image + - test_chat_multi_turn_multiple_images diff --git a/tests/verifications/conf/meta_reference.yaml b/tests/verifications/conf/meta_reference.yaml new file mode 100644 index 000000000..fb2680fe0 --- /dev/null +++ b/tests/verifications/conf/meta_reference.yaml @@ -0,0 +1,8 @@ +# LLAMA_STACK_PORT=5002 llama stack run meta-reference-gpu --env INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct --env INFERENCE_CHECKPOINT_DIR= +base_url: http://localhost:5002/v1/openai/v1 +api_key_var: foo +models: +- meta-llama/Llama-4-Scout-17B-16E-Instruct +model_display_names: + meta-llama/Llama-4-Scout-17B-16E-Instruct: Llama-4-Scout-Instruct +test_exclusions: {} diff --git a/tests/verifications/conf/together-llama-stack.yaml b/tests/verifications/conf/together-llama-stack.yaml index e49d82604..58cbcfa93 100644 --- a/tests/verifications/conf/together-llama-stack.yaml +++ b/tests/verifications/conf/together-llama-stack.yaml @@ -12,3 +12,6 @@ test_exclusions: together/meta-llama/Llama-3.3-70B-Instruct-Turbo: - test_chat_non_streaming_image - test_chat_streaming_image + - test_chat_multi_turn_multiple_images + - test_response_non_streaming_image + - test_response_non_streaming_multi_turn_image diff --git a/tests/verifications/conf/together.yaml b/tests/verifications/conf/together.yaml index 258616662..e8fb62ab9 100644 --- a/tests/verifications/conf/together.yaml +++ b/tests/verifications/conf/together.yaml @@ -12,3 +12,4 @@ test_exclusions: meta-llama/Llama-3.3-70B-Instruct-Turbo: - test_chat_non_streaming_image - test_chat_streaming_image + - test_chat_multi_turn_multiple_images diff --git a/tests/verifications/generate_report.py b/tests/verifications/generate_report.py index b39c3fd19..67ef14e90 100755 --- a/tests/verifications/generate_report.py +++ b/tests/verifications/generate_report.py @@ -1,16 +1,10 @@ +#!/usr/bin/env python3 + # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. - -# /// script -# requires-python = ">=3.10" -# dependencies = [ -# "pytest-json-report", -# "pyyaml", -# ] -# /// """ Test Report Generator @@ -24,7 +18,7 @@ Description: Configuration: - - Provider details (models, display names) are loaded from `tests/verifications/config.yaml`. + - Provider details (models, display names) are loaded from `tests/verifications/conf/*.yaml`. - Test cases are defined in YAML files within `tests/verifications/openai_api/fixtures/test_cases/`. - Test results are stored in `tests/verifications/test_results/`. @@ -56,7 +50,7 @@ import subprocess import time from collections import defaultdict from pathlib import Path -from typing import Any, DefaultDict, Dict, Set, Tuple +from typing import Any from tests.verifications.openai_api.fixtures.fixtures import _load_all_verification_configs @@ -67,16 +61,11 @@ RESULTS_DIR.mkdir(exist_ok=True) # Maximum number of test result files to keep per provider MAX_RESULTS_PER_PROVIDER = 1 -PROVIDER_ORDER = [ +DEFAULT_PROVIDERS = [ + "meta_reference", "together", "fireworks", - "groq", - "cerebras", "openai", - "together-llama-stack", - "fireworks-llama-stack", - "groq-llama-stack", - "openai-llama-stack", ] VERIFICATION_CONFIG = _load_all_verification_configs() @@ -117,7 +106,7 @@ def run_tests(provider, keyword=None): # Check if the JSON file was created if temp_json_file.exists(): - with open(temp_json_file, "r") as f: + with open(temp_json_file) as f: test_results = json.load(f) test_results["run_timestamp"] = timestamp @@ -142,9 +131,17 @@ def run_tests(provider, keyword=None): return None +def run_multiple_tests(providers_to_run: list[str], keyword: str | None): + """Runs tests for a list of providers.""" + print(f"Running tests for providers: {', '.join(providers_to_run)}") + for provider in providers_to_run: + run_tests(provider.strip(), keyword=keyword) + print("Finished running tests.") + + def parse_results( result_file, -) -> Tuple[DefaultDict[str, DefaultDict[str, Dict[str, bool]]], DefaultDict[str, Set[str]], Set[str], str]: +) -> tuple[defaultdict[str, defaultdict[str, dict[str, bool]]], defaultdict[str, set[str]], set[str], str]: """Parse a single test results file. Returns: @@ -159,13 +156,13 @@ def parse_results( # Return empty defaultdicts/set matching the type hint return defaultdict(lambda: defaultdict(dict)), defaultdict(set), set(), "" - with open(result_file, "r") as f: + with open(result_file) as f: results = json.load(f) # Initialize results dictionary with specific types - parsed_results: DefaultDict[str, DefaultDict[str, Dict[str, bool]]] = defaultdict(lambda: defaultdict(dict)) - providers_in_file: DefaultDict[str, Set[str]] = defaultdict(set) - tests_in_file: Set[str] = set() + parsed_results: defaultdict[str, defaultdict[str, dict[str, bool]]] = defaultdict(lambda: defaultdict(dict)) + providers_in_file: defaultdict[str, set[str]] = defaultdict(set) + tests_in_file: set[str] = set() # Extract provider from filename (e.g., "openai.json" -> "openai") provider: str = result_file.stem @@ -250,25 +247,11 @@ def parse_results( return parsed_results, providers_in_file, tests_in_file, run_timestamp_str -def get_all_result_files_by_provider(): - """Get all test result files, keyed by provider.""" - provider_results = {} - - result_files = list(RESULTS_DIR.glob("*.json")) - - for file in result_files: - provider = file.stem - if provider: - provider_results[provider] = file - - return provider_results - - def generate_report( - results_dict: Dict[str, Any], - providers: Dict[str, Set[str]], - all_tests: Set[str], - provider_timestamps: Dict[str, str], + results_dict: dict[str, Any], + providers: dict[str, set[str]], + all_tests: set[str], + provider_timestamps: dict[str, str], output_file=None, ): """Generate the markdown report. @@ -276,6 +259,7 @@ def generate_report( Args: results_dict: Aggregated results [provider][model][test_name] -> status. providers: Dict of all providers and their models {provider: {models}}. + The order of keys in this dict determines the report order. all_tests: Set of all test names found. provider_timestamps: Dict of provider to timestamp when tests were run output_file: Optional path to save the report. @@ -293,8 +277,8 @@ def generate_report( sorted_tests = sorted(all_tests) # Calculate counts for each base test name - base_test_case_counts: DefaultDict[str, int] = defaultdict(int) - base_test_name_map: Dict[str, str] = {} + base_test_case_counts: defaultdict[str, int] = defaultdict(int) + base_test_name_map: dict[str, str] = {} for test_name in sorted_tests: match = re.match(r"^(.*?)( \([^)]+\))?$", test_name) if match: @@ -353,22 +337,17 @@ def generate_report( passed_tests += 1 provider_totals[provider] = (provider_passed, provider_total) - # Add summary table (use passed-in providers dict) + # Add summary table (use the order from the providers dict keys) report.append("| Provider | Pass Rate | Tests Passed | Total Tests |") report.append("| --- | --- | --- | --- |") - for provider in [p for p in PROVIDER_ORDER if p in providers]: # Check against keys of passed-in dict - passed, total = provider_totals.get(provider, (0, 0)) - pass_rate = f"{(passed / total * 100):.1f}%" if total > 0 else "N/A" - report.append(f"| {provider.capitalize()} | {pass_rate} | {passed} | {total} |") - for provider in [p for p in providers if p not in PROVIDER_ORDER]: # Check against keys of passed-in dict + # Iterate through providers in the order they appear in the input dict + for provider in providers_sorted.keys(): passed, total = provider_totals.get(provider, (0, 0)) pass_rate = f"{(passed / total * 100):.1f}%" if total > 0 else "N/A" report.append(f"| {provider.capitalize()} | {pass_rate} | {passed} | {total} |") report.append("\n") - for provider in sorted( - providers_sorted.keys(), key=lambda p: (PROVIDER_ORDER.index(p) if p in PROVIDER_ORDER else float("inf"), p) - ): + for provider in providers_sorted.keys(): provider_models = providers_sorted[provider] # Use sorted models if not provider_models: continue @@ -461,60 +440,62 @@ def main(): "--providers", type=str, nargs="+", - help="Specify providers to test (comma-separated or space-separated, default: all)", + help="Specify providers to include/test (comma-separated or space-separated, default: uses DEFAULT_PROVIDERS)", ) parser.add_argument("--output", type=str, help="Output file location (default: tests/verifications/REPORT.md)") parser.add_argument("--k", type=str, help="Keyword expression to filter tests (passed to pytest -k)") args = parser.parse_args() all_results = {} - # Initialize collections to aggregate results in main - aggregated_providers = defaultdict(set) + final_providers_order = {} # Dictionary to store results, preserving processing order aggregated_tests = set() provider_timestamps = {} - if args.run_tests: - # Get list of available providers from command line or use detected providers - if args.providers: - # Handle both comma-separated and space-separated lists - test_providers = [] - for provider_arg in args.providers: - # Split by comma if commas are present - if "," in provider_arg: - test_providers.extend(provider_arg.split(",")) - else: - test_providers.append(provider_arg) - else: - # Default providers to test - test_providers = PROVIDER_ORDER - - for provider in test_providers: - provider = provider.strip() # Remove any whitespace - result_file = run_tests(provider, keyword=args.k) - if result_file: - # Parse and aggregate results - parsed_results, providers_in_file, tests_in_file, run_timestamp = parse_results(result_file) - all_results.update(parsed_results) - for prov, models in providers_in_file.items(): - aggregated_providers[prov].update(models) - if run_timestamp: - provider_timestamps[prov] = run_timestamp - aggregated_tests.update(tests_in_file) + # 1. Determine the desired list and order of providers + if args.providers: + desired_providers = [] + for provider_arg in args.providers: + desired_providers.extend([p.strip() for p in provider_arg.split(",")]) else: - # Use existing results - provider_result_files = get_all_result_files_by_provider() + desired_providers = DEFAULT_PROVIDERS # Use default order/list - for result_file in provider_result_files.values(): - # Parse and aggregate results - parsed_results, providers_in_file, tests_in_file, run_timestamp = parse_results(result_file) - all_results.update(parsed_results) - for prov, models in providers_in_file.items(): - aggregated_providers[prov].update(models) - if run_timestamp: - provider_timestamps[prov] = run_timestamp - aggregated_tests.update(tests_in_file) + # 2. Run tests if requested (using the desired provider list) + if args.run_tests: + run_multiple_tests(desired_providers, args.k) - generate_report(all_results, aggregated_providers, aggregated_tests, provider_timestamps, args.output) + for provider in desired_providers: + # Construct the expected result file path directly + result_file = RESULTS_DIR / f"{provider}.json" + + if result_file.exists(): # Check if the specific file exists + print(f"Loading results for {provider} from {result_file}") + try: + parsed_data = parse_results(result_file) + parsed_results, providers_in_file, tests_in_file, run_timestamp = parsed_data + all_results.update(parsed_results) + aggregated_tests.update(tests_in_file) + + # Add models for this provider, ensuring it's added in the correct report order + if provider in providers_in_file: + if provider not in final_providers_order: + final_providers_order[provider] = set() + final_providers_order[provider].update(providers_in_file[provider]) + if run_timestamp != "Unknown": + provider_timestamps[provider] = run_timestamp + else: + print( + f"Warning: Provider '{provider}' found in desired list but not within its result file data ({result_file})." + ) + + except Exception as e: + print(f"Error parsing results for provider {provider} from {result_file}: {e}") + else: + # Only print warning if we expected results (i.e., provider was in the desired list) + print(f"Result file for desired provider '{provider}' not found at {result_file}. Skipping.") + + # 5. Generate the report using the filtered & ordered results + print(f"Final Provider Order for Report: {list(final_providers_order.keys())}") + generate_report(all_results, final_providers_order, aggregated_tests, provider_timestamps, args.output) if __name__ == "__main__": diff --git a/tests/verifications/openai-api-verification-run.yaml b/tests/verifications/openai-api-verification-run.yaml index 71885d058..d80aa3c75 100644 --- a/tests/verifications/openai-api-verification-run.yaml +++ b/tests/verifications/openai-api-verification-run.yaml @@ -1,10 +1,15 @@ +# This is a temporary run file because model names used by the verification tests +# are not quite consistent with various pre-existing distributions. +# version: '2' image_name: openai-api-verification apis: +- agents - inference - telemetry - tool_runtime - vector_io +- safety providers: inference: - provider_id: together @@ -16,12 +21,12 @@ providers: provider_type: remote::fireworks config: url: https://api.fireworks.ai/inference/v1 - api_key: ${env.FIREWORKS_API_KEY} + api_key: ${env.FIREWORKS_API_KEY:} - provider_id: groq provider_type: remote::groq config: url: https://api.groq.com - api_key: ${env.GROQ_API_KEY} + api_key: ${env.GROQ_API_KEY:} - provider_id: openai provider_type: remote::openai config: @@ -44,7 +49,20 @@ providers: config: service_name: "${env.OTEL_SERVICE_NAME:\u200B}" sinks: ${env.TELEMETRY_SINKS:console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/openai/trace_store.db} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/openai-api-verification}/trace_store.db + safety: + - provider_id: llama-guard + provider_type: inline::llama-guard + config: + excluded_categories: [] + agents: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + persistence_store: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/openai}/agents_store.db tool_runtime: - provider_id: brave-search provider_type: remote::brave-search diff --git a/tests/verifications/openai_api/conftest.py b/tests/verifications/openai_api/conftest.py new file mode 100644 index 000000000..e4f7f27a0 --- /dev/null +++ b/tests/verifications/openai_api/conftest.py @@ -0,0 +1,35 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from tests.verifications.openai_api.fixtures.fixtures import _load_all_verification_configs + + +def pytest_generate_tests(metafunc): + """Dynamically parametrize tests based on the selected provider and config.""" + if "model" in metafunc.fixturenames: + provider = metafunc.config.getoption("provider") + if not provider: + print("Warning: --provider not specified. Skipping model parametrization.") + metafunc.parametrize("model", []) + return + + try: + config_data = _load_all_verification_configs() + except (OSError, FileNotFoundError) as e: + print(f"ERROR loading verification configs: {e}") + config_data = {"providers": {}} + + provider_config = config_data.get("providers", {}).get(provider) + if provider_config: + models = provider_config.get("models", []) + if models: + metafunc.parametrize("model", models) + else: + print(f"Warning: No models found for provider '{provider}' in config.") + metafunc.parametrize("model", []) # Parametrize empty if no models found + else: + print(f"Warning: Provider '{provider}' not found in config. No models parametrized.") + metafunc.parametrize("model", []) # Parametrize empty if provider not found diff --git a/tests/verifications/openai_api/fixtures/fixtures.py b/tests/verifications/openai_api/fixtures/fixtures.py index 940b99b2a..a7328e5f6 100644 --- a/tests/verifications/openai_api/fixtures/fixtures.py +++ b/tests/verifications/openai_api/fixtures/fixtures.py @@ -5,14 +5,16 @@ # the root directory of this source tree. import os +import re from pathlib import Path import pytest import yaml from openai import OpenAI +# --- Helper Functions --- + -# --- Helper Function to Load Config --- def _load_all_verification_configs(): """Load and aggregate verification configs from the conf/ directory.""" # Note: Path is relative to *this* file (fixtures.py) @@ -31,7 +33,7 @@ def _load_all_verification_configs(): for config_path in yaml_files: provider_name = config_path.stem try: - with open(config_path, "r") as f: + with open(config_path) as f: provider_config = yaml.safe_load(f) if provider_config: all_provider_configs[provider_name] = provider_config @@ -39,12 +41,35 @@ def _load_all_verification_configs(): # Log warning if possible, or just skip empty files silently print(f"Warning: Config file {config_path} is empty or invalid.") except Exception as e: - raise IOError(f"Error loading config file {config_path}: {e}") from e + raise OSError(f"Error loading config file {config_path}: {e}") from e return {"providers": all_provider_configs} -# --- End Helper Function --- +def case_id_generator(case): + """Generate a test ID from the case's 'case_id' field, or use a default.""" + case_id = case.get("case_id") + if isinstance(case_id, str | int): + return re.sub(r"\\W|^(?=\\d)", "_", str(case_id)) + return None + + +def should_skip_test(verification_config, provider, model, test_name_base): + """Check if a test should be skipped based on config exclusions.""" + provider_config = verification_config.get("providers", {}).get(provider) + if not provider_config: + return False # No config for provider, don't skip + + exclusions = provider_config.get("test_exclusions", {}).get(model, []) + return test_name_base in exclusions + + +# Helper to get the base test name from the request object +def get_base_test_name(request): + return request.node.originalname + + +# --- End Helper Functions --- @pytest.fixture(scope="session") @@ -52,7 +77,7 @@ def verification_config(): """Pytest fixture to provide the loaded verification config.""" try: return _load_all_verification_configs() - except (FileNotFoundError, IOError) as e: + except (OSError, FileNotFoundError) as e: pytest.fail(str(e)) # Fail test collection if config loading fails diff --git a/tests/verifications/openai_api/fixtures/images/vision_test_1.jpg b/tests/verifications/openai_api/fixtures/images/vision_test_1.jpg new file mode 100644 index 000000000..32fd0c0e3 Binary files /dev/null and b/tests/verifications/openai_api/fixtures/images/vision_test_1.jpg differ diff --git a/tests/verifications/openai_api/fixtures/images/vision_test_2.jpg b/tests/verifications/openai_api/fixtures/images/vision_test_2.jpg new file mode 100644 index 000000000..f9c28e3d5 Binary files /dev/null and b/tests/verifications/openai_api/fixtures/images/vision_test_2.jpg differ diff --git a/tests/verifications/openai_api/fixtures/images/vision_test_3.jpg b/tests/verifications/openai_api/fixtures/images/vision_test_3.jpg new file mode 100644 index 000000000..63165ea86 Binary files /dev/null and b/tests/verifications/openai_api/fixtures/images/vision_test_3.jpg differ diff --git a/tests/verifications/openai_api/fixtures/load.py b/tests/verifications/openai_api/fixtures/load.py index 98580b2a1..0184ee146 100644 --- a/tests/verifications/openai_api/fixtures/load.py +++ b/tests/verifications/openai_api/fixtures/load.py @@ -12,5 +12,5 @@ import yaml def load_test_cases(name: str): fixture_dir = Path(__file__).parent / "test_cases" yaml_path = fixture_dir / f"{name}.yaml" - with open(yaml_path, "r") as f: + with open(yaml_path) as f: return yaml.safe_load(f) diff --git a/tests/verifications/openai_api/fixtures/test_cases/chat_completion.yaml b/tests/verifications/openai_api/fixtures/test_cases/chat_completion.yaml index 78ea8245d..0c9f1fe9e 100644 --- a/tests/verifications/openai_api/fixtures/test_cases/chat_completion.yaml +++ b/tests/verifications/openai_api/fixtures/test_cases/chat_completion.yaml @@ -15,6 +15,52 @@ test_chat_basic: S? role: user output: Saturn +test_chat_input_validation: + test_name: test_chat_input_validation + test_params: + case: + - case_id: "messages_missing" + input: + messages: [] + output: + error: + status_code: 400 + - case_id: "messages_role_invalid" + input: + messages: + - content: Which planet do humans live on? + role: fake_role + output: + error: + status_code: 400 + - case_id: "tool_choice_invalid" + input: + messages: + - content: Which planet do humans live on? + role: user + tool_choice: invalid + output: + error: + status_code: 400 + - case_id: "tool_choice_no_tools" + input: + messages: + - content: Which planet do humans live on? + role: user + tool_choice: required + output: + error: + status_code: 400 + - case_id: "tools_type_invalid" + input: + messages: + - content: Which planet do humans live on? + role: user + tools: + - type: invalid + output: + error: + status_code: 400 test_chat_image: test_name: test_chat_image test_params: @@ -131,3 +177,221 @@ test_tool_calling: type: object type: function output: get_weather_tool_call + +test_chat_multi_turn_tool_calling: + test_name: test_chat_multi_turn_tool_calling + test_params: + case: + - case_id: "text_then_weather_tool" + input: + messages: + - - role: user + content: "What's the name of the Sun in latin?" + - - role: user + content: "What's the weather like in San Francisco?" + tools: + - function: + description: Get the current weather + name: get_weather + parameters: + type: object + properties: + location: + description: "The city and state (both required), e.g. San Francisco, CA." + type: string + required: ["location"] + type: function + tool_responses: + - response: "{'response': '70 degrees and foggy'}" + expected: + - num_tool_calls: 0 + answer: ["sol"] + - num_tool_calls: 1 + tool_name: get_weather + tool_arguments: + location: "San Francisco, CA" + - num_tool_calls: 0 + answer: ["foggy", "70 degrees"] + - case_id: "weather_tool_then_text" + input: + messages: + - - role: user + content: "What's the weather like in San Francisco?" + tools: + - function: + description: Get the current weather + name: get_weather + parameters: + type: object + properties: + location: + description: "The city and state (both required), e.g. San Francisco, CA." + type: string + required: ["location"] + type: function + tool_responses: + - response: "{'response': '70 degrees and foggy'}" + expected: + - num_tool_calls: 1 + tool_name: get_weather + tool_arguments: + location: "San Francisco, CA" + - num_tool_calls: 0 + answer: ["foggy", "70 degrees"] + - case_id: "add_product_tool" + input: + messages: + - - role: user + content: "Please add a new product with name 'Widget', price 19.99, in stock, and tags ['new', 'sale'] and give me the product id." + tools: + - function: + description: Add a new product + name: addProduct + parameters: + type: object + properties: + name: + description: "Name of the product" + type: string + price: + description: "Price of the product" + type: number + inStock: + description: "Availability status of the product." + type: boolean + tags: + description: "List of product tags" + type: array + items: + type: string + required: ["name", "price", "inStock"] + type: function + tool_responses: + - response: "{'response': 'Successfully added product with id: 123'}" + expected: + - num_tool_calls: 1 + tool_name: addProduct + tool_arguments: + name: "Widget" + price: 19.99 + inStock: true + tags: + - "new" + - "sale" + - num_tool_calls: 0 + answer: ["123", "product id: 123"] + - case_id: "get_then_create_event_tool" + input: + messages: + - - role: system + content: "Todays date is 2025-03-01." + - role: user + content: "Do i have any meetings on March 3rd at 10 am? Yes or no?" + - - role: user + content: "Alright then, Create an event named 'Team Building', scheduled for that time same time, in the 'Main Conference Room' and add Alice, Bob, Charlie to it. Give me the created event id." + tools: + - function: + description: Create a new event + name: create_event + parameters: + type: object + properties: + name: + description: "Name of the event" + type: string + date: + description: "Date of the event in ISO format" + type: string + time: + description: "Event Time (HH:MM)" + type: string + location: + description: "Location of the event" + type: string + participants: + description: "List of participant names" + type: array + items: + type: string + required: ["name", "date", "time", "location", "participants"] + type: function + - function: + description: Get an event by date and time + name: get_event + parameters: + type: object + properties: + date: + description: "Date of the event in ISO format" + type: string + time: + description: "Event Time (HH:MM)" + type: string + required: ["date", "time"] + type: function + tool_responses: + - response: "{'response': 'No events found for 2025-03-03 at 10:00'}" + - response: "{'response': 'Successfully created new event with id: e_123'}" + expected: + - num_tool_calls: 1 + tool_name: get_event + tool_arguments: + date: "2025-03-03" + time: "10:00" + - num_tool_calls: 0 + answer: ["no", "no events found", "no meetings"] + - num_tool_calls: 1 + tool_name: create_event + tool_arguments: + name: "Team Building" + date: "2025-03-03" + time: "10:00" + location: "Main Conference Room" + participants: + - "Alice" + - "Bob" + - "Charlie" + - num_tool_calls: 0 + answer: ["e_123", "event id: e_123"] + - case_id: "compare_monthly_expense_tool" + input: + messages: + - - role: system + content: "Todays date is 2025-03-01." + - role: user + content: "what was my monthly expense in Jan of this year?" + - - role: user + content: "Was it less than Feb of last year? Only answer with yes or no." + tools: + - function: + description: Get monthly expense summary + name: getMonthlyExpenseSummary + parameters: + type: object + properties: + month: + description: "Month of the year (1-12)" + type: integer + year: + description: "Year" + type: integer + required: ["month", "year"] + type: function + tool_responses: + - response: "{'response': 'Total expenses for January 2025: $1000'}" + - response: "{'response': 'Total expenses for February 2024: $2000'}" + expected: + - num_tool_calls: 1 + tool_name: getMonthlyExpenseSummary + tool_arguments: + month: 1 + year: 2025 + - num_tool_calls: 0 + answer: ["1000", "$1,000", "1,000"] + - num_tool_calls: 1 + tool_name: getMonthlyExpenseSummary + tool_arguments: + month: 2 + year: 2024 + - num_tool_calls: 0 + answer: ["yes"] diff --git a/tests/verifications/openai_api/fixtures/test_cases/responses.yaml b/tests/verifications/openai_api/fixtures/test_cases/responses.yaml new file mode 100644 index 000000000..f235b2ea8 --- /dev/null +++ b/tests/verifications/openai_api/fixtures/test_cases/responses.yaml @@ -0,0 +1,65 @@ +test_response_basic: + test_name: test_response_basic + test_params: + case: + - case_id: "earth" + input: "Which planet do humans live on?" + output: "earth" + - case_id: "saturn" + input: "Which planet has rings around it with a name starting with letter S?" + output: "saturn" + +test_response_multi_turn: + test_name: test_response_multi_turn + test_params: + case: + - case_id: "earth" + turns: + - input: "Which planet do humans live on?" + output: "earth" + - input: "What is the name of the planet from your previous response?" + output: "earth" + +test_response_web_search: + test_name: test_response_web_search + test_params: + case: + - case_id: "llama_experts" + input: "How many experts does the Llama 4 Maverick model have?" + tools: + - type: web_search + search_context_size: "low" + output: "128" + +test_response_image: + test_name: test_response_image + test_params: + case: + - case_id: "llama_image" + input: + - role: user + content: + - type: input_text + text: "Identify the type of animal in this image." + - type: input_image + image_url: "https://upload.wikimedia.org/wikipedia/commons/f/f7/Llamas%2C_Vernagt-Stausee%2C_Italy.jpg" + output: "llama" + +test_response_multi_turn_image: + test_name: test_response_multi_turn_image + test_params: + case: + - case_id: "llama_image_search" + turns: + - input: + - role: user + content: + - type: input_text + text: "What type of animal is in this image? Please respond with a single word that starts with the letter 'L'." + - type: input_image + image_url: "https://upload.wikimedia.org/wikipedia/commons/f/f7/Llamas%2C_Vernagt-Stausee%2C_Italy.jpg" + output: "llama" + - input: "Search the web using the search tool for the animal from the previous response. Your search query should be a single phrase that includes the animal's name and the words 'maverick' and 'scout'." + tools: + - type: web_search + output: "model" diff --git a/tests/verifications/openai_api/test_chat_completion.py b/tests/verifications/openai_api/test_chat_completion.py index 6aee29c3a..64e49d352 100644 --- a/tests/verifications/openai_api/test_chat_completion.py +++ b/tests/verifications/openai_api/test_chat_completion.py @@ -4,68 +4,41 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import base64 +import copy import json -import re +from pathlib import Path from typing import Any import pytest +from openai import APIError from pydantic import BaseModel -from tests.verifications.openai_api.fixtures.fixtures import _load_all_verification_configs +from tests.verifications.openai_api.fixtures.fixtures import ( + case_id_generator, + get_base_test_name, + should_skip_test, +) from tests.verifications.openai_api.fixtures.load import load_test_cases chat_completion_test_cases = load_test_cases("chat_completion") - -def case_id_generator(case): - """Generate a test ID from the case's 'case_id' field, or use a default.""" - case_id = case.get("case_id") - if isinstance(case_id, (str, int)): - return re.sub(r"\\W|^(?=\\d)", "_", str(case_id)) - return None +THIS_DIR = Path(__file__).parent -def pytest_generate_tests(metafunc): - """Dynamically parametrize tests based on the selected provider and config.""" - if "model" in metafunc.fixturenames: - provider = metafunc.config.getoption("provider") - if not provider: - print("Warning: --provider not specified. Skipping model parametrization.") - metafunc.parametrize("model", []) - return - - try: - config_data = _load_all_verification_configs() - except (FileNotFoundError, IOError) as e: - print(f"ERROR loading verification configs: {e}") - config_data = {"providers": {}} - - provider_config = config_data.get("providers", {}).get(provider) - if provider_config: - models = provider_config.get("models", []) - if models: - metafunc.parametrize("model", models) - else: - print(f"Warning: No models found for provider '{provider}' in config.") - metafunc.parametrize("model", []) # Parametrize empty if no models found - else: - print(f"Warning: Provider '{provider}' not found in config. No models parametrized.") - metafunc.parametrize("model", []) # Parametrize empty if provider not found - - -def should_skip_test(verification_config, provider, model, test_name_base): - """Check if a test should be skipped based on config exclusions.""" - provider_config = verification_config.get("providers", {}).get(provider) - if not provider_config: - return False # No config for provider, don't skip - - exclusions = provider_config.get("test_exclusions", {}).get(model, []) - return test_name_base in exclusions - - -# Helper to get the base test name from the request object -def get_base_test_name(request): - return request.node.originalname +@pytest.fixture +def multi_image_data(): + files = [ + THIS_DIR / "fixtures/images/vision_test_1.jpg", + THIS_DIR / "fixtures/images/vision_test_2.jpg", + THIS_DIR / "fixtures/images/vision_test_3.jpg", + ] + encoded_files = [] + for file in files: + with open(file, "rb") as image_file: + base64_data = base64.b64encode(image_file.read()).decode("utf-8") + encoded_files.append(f"data:image/jpeg;base64,{base64_data}") + return encoded_files # --- Test Functions --- @@ -114,6 +87,50 @@ def test_chat_streaming_basic(request, openai_client, model, provider, verificat assert case["output"].lower() in content.lower() +@pytest.mark.parametrize( + "case", + chat_completion_test_cases["test_chat_input_validation"]["test_params"]["case"], + ids=case_id_generator, +) +def test_chat_non_streaming_error_handling(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + with pytest.raises(APIError) as e: + openai_client.chat.completions.create( + model=model, + messages=case["input"]["messages"], + stream=False, + tool_choice=case["input"]["tool_choice"] if "tool_choice" in case["input"] else None, + tools=case["input"]["tools"] if "tools" in case["input"] else None, + ) + assert case["output"]["error"]["status_code"] == e.value.status_code + + +@pytest.mark.parametrize( + "case", + chat_completion_test_cases["test_chat_input_validation"]["test_params"]["case"], + ids=case_id_generator, +) +def test_chat_streaming_error_handling(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + with pytest.raises(APIError) as e: + response = openai_client.chat.completions.create( + model=model, + messages=case["input"]["messages"], + stream=True, + tool_choice=case["input"]["tool_choice"] if "tool_choice" in case["input"] else None, + tools=case["input"]["tools"] if "tools" in case["input"] else None, + ) + for _chunk in response: + pass + assert str(case["output"]["error"]["status_code"]) in e.value.message + + @pytest.mark.parametrize( "case", chat_completion_test_cases["test_chat_image"]["test_params"]["case"], @@ -243,43 +260,373 @@ def test_chat_streaming_tool_calling(request, openai_client, model, provider, ve stream=True, ) - # Accumulate partial tool_calls here - tool_calls_buffer = {} - current_id = None - # Process streaming chunks - for chunk in stream: - choice = chunk.choices[0] - delta = choice.delta - - if delta.tool_calls is None: - continue - - for tool_call_delta in delta.tool_calls: - if tool_call_delta.id: - current_id = tool_call_delta.id - call_id = current_id - func_delta = tool_call_delta.function - - if call_id not in tool_calls_buffer: - tool_calls_buffer[call_id] = { - "id": call_id, - "type": tool_call_delta.type, - "name": func_delta.name, - "arguments": "", - } - - if func_delta.arguments: - tool_calls_buffer[call_id]["arguments"] += func_delta.arguments - + _, tool_calls_buffer = _accumulate_streaming_tool_calls(stream) assert len(tool_calls_buffer) == 1 - for call in tool_calls_buffer.values(): + for call in tool_calls_buffer: assert len(call["id"]) > 0 - assert call["name"] == "get_weather" + function = call["function"] + assert function["name"] == "get_weather" - args_dict = json.loads(call["arguments"]) + args_dict = json.loads(function["arguments"]) assert "san francisco" in args_dict["location"].lower() +@pytest.mark.parametrize( + "case", + chat_completion_test_cases["test_tool_calling"]["test_params"]["case"], # Reusing existing case for now + ids=case_id_generator, +) +def test_chat_non_streaming_tool_choice_required(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + response = openai_client.chat.completions.create( + model=model, + messages=case["input"]["messages"], + tools=case["input"]["tools"], + tool_choice="required", # Force tool call + stream=False, + ) + + assert response.choices[0].message.role == "assistant" + assert len(response.choices[0].message.tool_calls) > 0, "Expected tool call when tool_choice='required'" + expected_tool_name = case["input"]["tools"][0]["function"]["name"] + assert response.choices[0].message.tool_calls[0].function.name == expected_tool_name + + +@pytest.mark.parametrize( + "case", + chat_completion_test_cases["test_tool_calling"]["test_params"]["case"], # Reusing existing case for now + ids=case_id_generator, +) +def test_chat_streaming_tool_choice_required(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + stream = openai_client.chat.completions.create( + model=model, + messages=case["input"]["messages"], + tools=case["input"]["tools"], + tool_choice="required", # Force tool call + stream=True, + ) + + _, tool_calls_buffer = _accumulate_streaming_tool_calls(stream) + + assert len(tool_calls_buffer) > 0, "Expected tool call when tool_choice='required'" + expected_tool_name = case["input"]["tools"][0]["function"]["name"] + assert any(call["function"]["name"] == expected_tool_name for call in tool_calls_buffer), ( + f"Expected tool call '{expected_tool_name}' not found in stream" + ) + + +@pytest.mark.parametrize( + "case", + chat_completion_test_cases["test_tool_calling"]["test_params"]["case"], # Reusing existing case for now + ids=case_id_generator, +) +def test_chat_non_streaming_tool_choice_none(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + response = openai_client.chat.completions.create( + model=model, + messages=case["input"]["messages"], + tools=case["input"]["tools"], + tool_choice="none", + stream=False, + ) + + assert response.choices[0].message.role == "assistant" + assert response.choices[0].message.tool_calls is None, "Expected no tool calls when tool_choice='none'" + assert response.choices[0].message.content is not None, "Expected content when tool_choice='none'" + + +@pytest.mark.parametrize( + "case", + chat_completion_test_cases["test_tool_calling"]["test_params"]["case"], # Reusing existing case for now + ids=case_id_generator, +) +def test_chat_streaming_tool_choice_none(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + stream = openai_client.chat.completions.create( + model=model, + messages=case["input"]["messages"], + tools=case["input"]["tools"], + tool_choice="none", + stream=True, + ) + + content = "" + for chunk in stream: + delta = chunk.choices[0].delta + if delta.content: + content += delta.content + assert not delta.tool_calls, "Expected no tool call chunks when tool_choice='none'" + + assert len(content) > 0, "Expected content when tool_choice='none'" + + +@pytest.mark.parametrize( + "case", + chat_completion_test_cases.get("test_chat_multi_turn_tool_calling", {}).get("test_params", {}).get("case", []), + ids=case_id_generator, +) +def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case): + """ + Test cases for multi-turn tool calling. + Tool calls are asserted. + Tool responses are provided in the test case. + Final response is asserted. + """ + + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + # Create a copy of the messages list to avoid modifying the original + messages = [] + tools = case["input"]["tools"] + # Use deepcopy to prevent modification across runs/parametrization + expected_results = copy.deepcopy(case["expected"]) + tool_responses = copy.deepcopy(case.get("tool_responses", [])) + input_messages_turns = copy.deepcopy(case["input"]["messages"]) + + # keep going until either + # 1. we have messages to test in multi-turn + # 2. no messages but last message is tool response + while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1]["role"] == "tool"): + # do not take new messages if last message is tool response + if len(messages) == 0 or messages[-1]["role"] != "tool": + new_messages = input_messages_turns.pop(0) + # Ensure new_messages is a list of message objects + if isinstance(new_messages, list): + messages.extend(new_messages) + else: + # If it's a single message object, add it directly + messages.append(new_messages) + + # --- API Call --- + response = openai_client.chat.completions.create( + model=model, + messages=messages, + tools=tools, + stream=False, + ) + + # --- Process Response --- + assistant_message = response.choices[0].message + messages.append(assistant_message.model_dump(exclude_unset=True)) + + assert assistant_message.role == "assistant" + + # Get the expected result data + expected = expected_results.pop(0) + num_tool_calls = expected["num_tool_calls"] + + # --- Assertions based on expected result --- + assert len(assistant_message.tool_calls or []) == num_tool_calls, ( + f"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}" + ) + + if num_tool_calls > 0: + tool_call = assistant_message.tool_calls[0] + assert tool_call.function.name == expected["tool_name"], ( + f"Expected tool '{expected['tool_name']}', got '{tool_call.function.name}'" + ) + # Parse the JSON string arguments before comparing + actual_arguments = json.loads(tool_call.function.arguments) + assert actual_arguments == expected["tool_arguments"], ( + f"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'" + ) + + # Prepare and append the tool response for the next turn + tool_response = tool_responses.pop(0) + messages.append( + { + "role": "tool", + "tool_call_id": tool_call.id, + "content": tool_response["response"], + } + ) + else: + assert assistant_message.content is not None, "Expected content, but none received." + expected_answers = expected["answer"] # This is now a list + content_lower = assistant_message.content.lower() + assert any(ans.lower() in content_lower for ans in expected_answers), ( + f"Expected one of {expected_answers} in content, but got: '{assistant_message.content}'" + ) + + +@pytest.mark.parametrize( + "case", + chat_completion_test_cases.get("test_chat_multi_turn_tool_calling", {}).get("test_params", {}).get("case", []), + ids=case_id_generator, +) +def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case): + """ """ + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + messages = [] + tools = case["input"]["tools"] + expected_results = copy.deepcopy(case["expected"]) + tool_responses = copy.deepcopy(case.get("tool_responses", [])) + input_messages_turns = copy.deepcopy(case["input"]["messages"]) + + while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1]["role"] == "tool"): + if len(messages) == 0 or messages[-1]["role"] != "tool": + new_messages = input_messages_turns.pop(0) + if isinstance(new_messages, list): + messages.extend(new_messages) + else: + messages.append(new_messages) + + # --- API Call (Streaming) --- + stream = openai_client.chat.completions.create( + model=model, + messages=messages, + tools=tools, + stream=True, + ) + + # --- Process Stream --- + accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream) + + # --- Construct Assistant Message for History --- + assistant_message_dict = {"role": "assistant"} + if accumulated_content: + assistant_message_dict["content"] = accumulated_content + if accumulated_tool_calls: + assistant_message_dict["tool_calls"] = accumulated_tool_calls + + messages.append(assistant_message_dict) + + # --- Assertions --- + expected = expected_results.pop(0) + num_tool_calls = expected["num_tool_calls"] + + assert len(accumulated_tool_calls or []) == num_tool_calls, ( + f"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}" + ) + + if num_tool_calls > 0: + # Use the first accumulated tool call for assertion + tool_call = accumulated_tool_calls[0] + assert tool_call["function"]["name"] == expected["tool_name"], ( + f"Expected tool '{expected['tool_name']}', got '{tool_call['function']['name']}'" + ) + # Parse the accumulated arguments string for comparison + actual_arguments = json.loads(tool_call["function"]["arguments"]) + assert actual_arguments == expected["tool_arguments"], ( + f"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'" + ) + + # Prepare and append the tool response for the next turn + tool_response = tool_responses.pop(0) + messages.append( + { + "role": "tool", + "tool_call_id": tool_call["id"], + "content": tool_response["response"], + } + ) + else: + assert accumulated_content is not None and accumulated_content != "", "Expected content, but none received." + expected_answers = expected["answer"] + content_lower = accumulated_content.lower() + assert any(ans.lower() in content_lower for ans in expected_answers), ( + f"Expected one of {expected_answers} in content, but got: '{accumulated_content}'" + ) + + +@pytest.mark.parametrize("stream", [False, True], ids=["stream=False", "stream=True"]) +def test_chat_multi_turn_multiple_images( + request, openai_client, model, provider, verification_config, multi_image_data, stream +): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + messages_turn1 = [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": multi_image_data[0], + }, + }, + { + "type": "image_url", + "image_url": { + "url": multi_image_data[1], + }, + }, + { + "type": "text", + "text": "What furniture is in the first image that is not in the second image?", + }, + ], + }, + ] + + # First API call + response1 = openai_client.chat.completions.create( + model=model, + messages=messages_turn1, + stream=stream, + ) + if stream: + message_content1 = "" + for chunk in response1: + message_content1 += chunk.choices[0].delta.content or "" + else: + message_content1 = response1.choices[0].message.content + assert len(message_content1) > 0 + assert any(expected in message_content1.lower().strip() for expected in {"chair", "table"}), message_content1 + + # Prepare messages for the second turn + messages_turn2 = messages_turn1 + [ + {"role": "assistant", "content": message_content1}, + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": multi_image_data[2], + }, + }, + {"type": "text", "text": "What is in this image that is also in the first image?"}, + ], + }, + ] + + # Second API call + response2 = openai_client.chat.completions.create( + model=model, + messages=messages_turn2, + stream=stream, + ) + if stream: + message_content2 = "" + for chunk in response2: + message_content2 += chunk.choices[0].delta.content or "" + else: + message_content2 = response2.choices[0].message.content + assert len(message_content2) > 0 + assert any(expected in message_content2.lower().strip() for expected in {"bed"}), message_content2 + + # --- Helper functions (structured output validation) --- @@ -324,3 +671,47 @@ def validate_structured_output(maybe_json_content: str, schema_name: str) -> Non assert len(structured_output.participants) == 2 elif schema_name == "valid_math_reasoning": assert len(structured_output.final_answer) > 0 + + +def _accumulate_streaming_tool_calls(stream): + """Accumulates tool calls and content from a streaming ChatCompletion response.""" + tool_calls_buffer = {} + current_id = None + full_content = "" # Initialize content accumulator + # Process streaming chunks + for chunk in stream: + choice = chunk.choices[0] + delta = choice.delta + + # Accumulate content + if delta.content: + full_content += delta.content + + if delta.tool_calls is None: + continue + + for tool_call_delta in delta.tool_calls: + if tool_call_delta.id: + current_id = tool_call_delta.id + call_id = current_id + # Skip if no ID seen yet for this tool call delta + if not call_id: + continue + func_delta = tool_call_delta.function + + if call_id not in tool_calls_buffer: + tool_calls_buffer[call_id] = { + "id": call_id, + "type": "function", # Assume function type + "function": {"name": None, "arguments": ""}, # Nested structure + } + + # Accumulate name and arguments into the nested function dict + if func_delta: + if func_delta.name: + tool_calls_buffer[call_id]["function"]["name"] = func_delta.name + if func_delta.arguments: + tool_calls_buffer[call_id]["function"]["arguments"] += func_delta.arguments + + # Return content and tool calls as a list + return full_content, list(tool_calls_buffer.values()) diff --git a/tests/verifications/openai_api/test_responses.py b/tests/verifications/openai_api/test_responses.py new file mode 100644 index 000000000..cc7ec320c --- /dev/null +++ b/tests/verifications/openai_api/test_responses.py @@ -0,0 +1,166 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + + +import pytest + +from tests.verifications.openai_api.fixtures.fixtures import ( + case_id_generator, + get_base_test_name, + should_skip_test, +) +from tests.verifications.openai_api.fixtures.load import load_test_cases + +responses_test_cases = load_test_cases("responses") + + +@pytest.mark.parametrize( + "case", + responses_test_cases["test_response_basic"]["test_params"]["case"], + ids=case_id_generator, +) +def test_response_non_streaming_basic(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + response = openai_client.responses.create( + model=model, + input=case["input"], + stream=False, + ) + output_text = response.output_text.lower().strip() + assert len(output_text) > 0 + assert case["output"].lower() in output_text + + retrieved_response = openai_client.responses.retrieve(response_id=response.id) + assert retrieved_response.output_text == response.output_text + + next_response = openai_client.responses.create( + model=model, input="Repeat your previous response in all caps.", previous_response_id=response.id + ) + next_output_text = next_response.output_text.strip() + assert case["output"].upper() in next_output_text + + +@pytest.mark.parametrize( + "case", + responses_test_cases["test_response_basic"]["test_params"]["case"], + ids=case_id_generator, +) +def test_response_streaming_basic(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + response = openai_client.responses.create( + model=model, + input=case["input"], + stream=True, + ) + streamed_content = [] + response_id = "" + for chunk in response: + if chunk.type == "response.completed": + response_id = chunk.response.id + streamed_content.append(chunk.response.output_text.strip()) + + assert len(streamed_content) > 0 + assert case["output"].lower() in "".join(streamed_content).lower() + + retrieved_response = openai_client.responses.retrieve(response_id=response_id) + assert retrieved_response.output_text == "".join(streamed_content) + + +@pytest.mark.parametrize( + "case", + responses_test_cases["test_response_multi_turn"]["test_params"]["case"], + ids=case_id_generator, +) +def test_response_non_streaming_multi_turn(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + previous_response_id = None + for turn in case["turns"]: + response = openai_client.responses.create( + model=model, + input=turn["input"], + previous_response_id=previous_response_id, + tools=turn["tools"] if "tools" in turn else None, + ) + previous_response_id = response.id + output_text = response.output_text.lower() + assert turn["output"].lower() in output_text + + +@pytest.mark.parametrize( + "case", + responses_test_cases["test_response_web_search"]["test_params"]["case"], + ids=case_id_generator, +) +def test_response_non_streaming_web_search(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + response = openai_client.responses.create( + model=model, + input=case["input"], + tools=case["tools"], + stream=False, + ) + assert len(response.output) > 1 + assert response.output[0].type == "web_search_call" + assert response.output[0].status == "completed" + assert response.output[1].type == "message" + assert response.output[1].status == "completed" + assert response.output[1].role == "assistant" + assert len(response.output[1].content) > 0 + assert case["output"].lower() in response.output_text.lower().strip() + + +@pytest.mark.parametrize( + "case", + responses_test_cases["test_response_image"]["test_params"]["case"], + ids=case_id_generator, +) +def test_response_non_streaming_image(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + response = openai_client.responses.create( + model=model, + input=case["input"], + stream=False, + ) + output_text = response.output_text.lower() + assert case["output"].lower() in output_text + + +@pytest.mark.parametrize( + "case", + responses_test_cases["test_response_multi_turn_image"]["test_params"]["case"], + ids=case_id_generator, +) +def test_response_non_streaming_multi_turn_image(request, openai_client, model, provider, verification_config, case): + test_name_base = get_base_test_name(request) + if should_skip_test(verification_config, provider, model, test_name_base): + pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.") + + previous_response_id = None + for turn in case["turns"]: + response = openai_client.responses.create( + model=model, + input=turn["input"], + previous_response_id=previous_response_id, + tools=turn["tools"] if "tools" in turn else None, + ) + previous_response_id = response.id + output_text = response.output_text.lower() + assert turn["output"].lower() in output_text diff --git a/tests/verifications/test_results/fireworks.json b/tests/verifications/test_results/fireworks.json index 061e44c08..ef5cf142e 100644 --- a/tests/verifications/test_results/fireworks.json +++ b/tests/verifications/test_results/fireworks.json @@ -1,15 +1,15 @@ { - "created": 1744328795.171092, - "duration": 107.57908606529236, + "created": 1744918448.686489, + "duration": 254.68238854408264, "exitcode": 1, - "root": "/Users/erichuang/projects/llama-stack", + "root": "/home/erichuang/llama-stack", "environment": {}, "summary": { - "passed": 28, - "skipped": 2, - "failed": 6, - "total": 36, - "collected": 36 + "passed": 40, + "skipped": 4, + "failed": 40, + "total": 84, + "collected": 84 }, "collectors": [ { @@ -29,182 +29,422 @@ { "nodeid": 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"test_chat_non_streaming_basic[accounts/fireworks/models/llama-v3p3-70b-instruct-earth]", @@ -231,21 +471,21 @@ "case_id": "earth" }, "setup": { - "duration": 0.2175025000469759, + "duration": 0.13845239393413067, "outcome": "passed" }, "call": { - "duration": 0.7433859170414507, + "duration": 1.3300942620262504, "outcome": "passed" }, "teardown": { - "duration": 0.0001592918997630477, + "duration": 0.00025453977286815643, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_basic[accounts/fireworks/models/llama-v3p3-70b-instruct-saturn]", - "lineno": 73, + "lineno": 95, "outcome": "passed", "keywords": [ "test_chat_non_streaming_basic[accounts/fireworks/models/llama-v3p3-70b-instruct-saturn]", @@ -264,21 +504,21 @@ "case_id": "saturn" }, "setup": { - "duration": 0.007383499993011355, + "duration": 0.0806605163961649, "outcome": "passed" }, "call": { - "duration": 0.5949292909353971, + "duration": 0.6202042903751135, "outcome": "passed" }, "teardown": { - "duration": 0.00015891704242676497, + "duration": 0.00026358477771282196, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_basic[accounts/fireworks/models/llama4-scout-instruct-basic-earth]", - "lineno": 73, + "lineno": 95, "outcome": "passed", "keywords": [ "test_chat_non_streaming_basic[accounts/fireworks/models/llama4-scout-instruct-basic-earth]", @@ -297,21 +537,21 @@ "case_id": "earth" }, "setup": { - "duration": 0.010730999987572432, + "duration": 0.07190297450870275, "outcome": "passed" }, "call": { - "duration": 0.8945954169612378, + "duration": 0.7458920907229185, "outcome": "passed" }, "teardown": { - "duration": 0.0003751249751076102, + "duration": 0.00024067144840955734, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_basic[accounts/fireworks/models/llama4-scout-instruct-basic-saturn]", - "lineno": 73, + "lineno": 95, "outcome": "passed", "keywords": [ "test_chat_non_streaming_basic[accounts/fireworks/models/llama4-scout-instruct-basic-saturn]", @@ -330,21 +570,21 @@ "case_id": "saturn" }, "setup": { - "duration": 0.01665666699409485, + "duration": 0.07551384158432484, "outcome": "passed" }, "call": { - "duration": 0.907927209045738, + "duration": 0.6140249809250236, "outcome": "passed" }, "teardown": { - "duration": 0.00024874997325241566, + "duration": 0.00024476367980241776, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_basic[accounts/fireworks/models/llama4-maverick-instruct-basic-earth]", - "lineno": 73, + "lineno": 95, "outcome": "passed", "keywords": [ "test_chat_non_streaming_basic[accounts/fireworks/models/llama4-maverick-instruct-basic-earth]", @@ -363,21 +603,21 @@ "case_id": "earth" }, "setup": { - "duration": 0.01039199996739626, + "duration": 0.07434738799929619, "outcome": "passed" }, "call": { - "duration": 0.5971567500382662, + "duration": 1.6738943997770548, "outcome": "passed" }, "teardown": { - "duration": 0.0003488330403342843, + "duration": 0.000227426178753376, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_basic[accounts/fireworks/models/llama4-maverick-instruct-basic-saturn]", - "lineno": 73, + "lineno": 95, "outcome": "passed", "keywords": [ "test_chat_non_streaming_basic[accounts/fireworks/models/llama4-maverick-instruct-basic-saturn]", @@ -396,21 +636,21 @@ "case_id": "saturn" }, "setup": { - "duration": 0.018627874902449548, + "duration": 0.07130288146436214, "outcome": "passed" }, "call": { - "duration": 2.0586736251134425, + "duration": 1.337895905598998, "outcome": "passed" }, "teardown": { - "duration": 0.00046974990982562304, + "duration": 0.00028038304299116135, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[accounts/fireworks/models/llama-v3p3-70b-instruct-earth]", - "lineno": 92, + "lineno": 114, "outcome": "passed", "keywords": [ "test_chat_streaming_basic[accounts/fireworks/models/llama-v3p3-70b-instruct-earth]", @@ -429,21 +669,21 @@ "case_id": "earth" }, "setup": { - "duration": 0.01706262503284961, + "duration": 0.0727478675544262, "outcome": "passed" }, "call": { - "duration": 0.6679969580145553, + "duration": 0.7670011632144451, "outcome": "passed" }, "teardown": { - "duration": 0.0004670419730246067, + "duration": 0.00023174844682216644, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[accounts/fireworks/models/llama-v3p3-70b-instruct-saturn]", - "lineno": 92, + "lineno": 114, "outcome": "passed", "keywords": [ "test_chat_streaming_basic[accounts/fireworks/models/llama-v3p3-70b-instruct-saturn]", @@ -462,21 +702,21 @@ "case_id": "saturn" }, "setup": { - "duration": 0.025956374942325056, + "duration": 0.07163545861840248, "outcome": "passed" }, "call": { - "duration": 2.052679874934256, + "duration": 0.7582714259624481, "outcome": "passed" }, "teardown": { - "duration": 0.00026958296075463295, + "duration": 0.00028524454683065414, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[accounts/fireworks/models/llama4-scout-instruct-basic-earth]", - "lineno": 92, + "lineno": 114, "outcome": "passed", "keywords": [ "test_chat_streaming_basic[accounts/fireworks/models/llama4-scout-instruct-basic-earth]", @@ -495,21 +735,21 @@ "case_id": "earth" }, "setup": { - "duration": 0.015856957994401455, + "duration": 0.08122281823307276, "outcome": "passed" }, "call": { - "duration": 0.3096678329166025, + "duration": 0.6061851140111685, "outcome": "passed" }, "teardown": { - "duration": 0.0007620420074090362, + "duration": 0.0002497304230928421, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[accounts/fireworks/models/llama4-scout-instruct-basic-saturn]", - "lineno": 92, + "lineno": 114, "outcome": "passed", "keywords": [ "test_chat_streaming_basic[accounts/fireworks/models/llama4-scout-instruct-basic-saturn]", @@ -528,21 +768,21 @@ "case_id": "saturn" }, "setup": { - "duration": 0.013509334065020084, + "duration": 0.07185561209917068, "outcome": "passed" }, "call": { - "duration": 0.5914681670255959, + "duration": 0.7516075978055596, "outcome": "passed" }, "teardown": { - "duration": 0.0002906669396907091, + "duration": 0.00026526860892772675, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[accounts/fireworks/models/llama4-maverick-instruct-basic-earth]", - "lineno": 92, + "lineno": 114, "outcome": "passed", "keywords": [ "test_chat_streaming_basic[accounts/fireworks/models/llama4-maverick-instruct-basic-earth]", @@ -561,21 +801,21 @@ "case_id": "earth" }, "setup": { - "duration": 0.013216375024057925, + "duration": 0.07012896798551083, "outcome": "passed" }, "call": { - "duration": 1.8804527079919353, + "duration": 1.8946502823382616, "outcome": "passed" }, "teardown": { - "duration": 0.0002026669681072235, + "duration": 0.0002452842891216278, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[accounts/fireworks/models/llama4-maverick-instruct-basic-saturn]", - "lineno": 92, + "lineno": 114, "outcome": "passed", "keywords": [ "test_chat_streaming_basic[accounts/fireworks/models/llama4-maverick-instruct-basic-saturn]", @@ -594,21 +834,21 @@ "case_id": "saturn" }, "setup": { - "duration": 0.00827441702131182, + "duration": 0.06955648958683014, "outcome": "passed" }, "call": { - "duration": 0.7407040420221165, + "duration": 1.0446623722091317, "outcome": "passed" }, "teardown": { - "duration": 0.0005084159784018993, + "duration": 0.00023738667368888855, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_image[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", - "lineno": 116, + "lineno": 138, "outcome": "skipped", "keywords": [ "test_chat_non_streaming_image[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", @@ -627,22 +867,22 @@ "case_id": "case0" }, "setup": { - "duration": 0.012424499960616231, + "duration": 0.07077906839549541, "outcome": "passed" }, "call": { - "duration": 0.00032762496266514063, + "duration": 0.00021365191787481308, "outcome": "skipped", - "longrepr": "('/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py', 125, 'Skipped: Skipping test_chat_non_streaming_image for model accounts/fireworks/models/llama-v3p3-70b-instruct on provider fireworks based on config.')" + "longrepr": "('/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py', 147, 'Skipped: Skipping test_chat_non_streaming_image for model accounts/fireworks/models/llama-v3p3-70b-instruct on provider fireworks based on config.')" }, "teardown": { - "duration": 0.00032416603062301874, + "duration": 0.00018982868641614914, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_image[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", - "lineno": 116, + "lineno": 138, "outcome": "passed", "keywords": [ "test_chat_non_streaming_image[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", @@ -661,21 +901,21 @@ "case_id": "case0" }, "setup": { - "duration": 0.02253958396613598, + "duration": 0.07118859142065048, "outcome": "passed" }, "call": { - "duration": 2.64042466704268, + "duration": 4.20654855389148, "outcome": "passed" }, "teardown": { - "duration": 0.0003636250039562583, + "duration": 0.00023640412837266922, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_image[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", - "lineno": 116, + "lineno": 138, "outcome": "passed", "keywords": [ "test_chat_non_streaming_image[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", @@ -694,21 +934,21 @@ "case_id": "case0" }, "setup": { - "duration": 0.014634749968536198, + "duration": 0.07351029943674803, "outcome": "passed" }, "call": { - "duration": 5.126485540997237, + "duration": 4.875292049720883, "outcome": "passed" }, "teardown": { - "duration": 0.0002988330088555813, + "duration": 0.0002571679651737213, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_image[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", - "lineno": 135, + "lineno": 157, "outcome": "skipped", "keywords": [ "test_chat_streaming_image[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", @@ -727,22 +967,22 @@ "case_id": "case0" }, "setup": { - "duration": 0.015854416065849364, + "duration": 0.07474396284669638, "outcome": "passed" }, "call": { - "duration": 0.00038058299105614424, + "duration": 0.0002510417252779007, "outcome": "skipped", - "longrepr": "('/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py', 144, 'Skipped: Skipping test_chat_streaming_image for model accounts/fireworks/models/llama-v3p3-70b-instruct on provider fireworks based on config.')" + "longrepr": "('/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py', 166, 'Skipped: Skipping test_chat_streaming_image for model accounts/fireworks/models/llama-v3p3-70b-instruct on provider fireworks based on config.')" }, "teardown": { - "duration": 0.0002689170651137829, + "duration": 0.00020200759172439575, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_image[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", - "lineno": 135, + "lineno": 157, "outcome": "passed", "keywords": [ "test_chat_streaming_image[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", @@ -761,21 +1001,21 @@ "case_id": "case0" }, "setup": { - "duration": 0.011205915943719447, + "duration": 0.07380561903119087, "outcome": "passed" }, "call": { - "duration": 3.2596546669956297, + "duration": 2.0082657346501946, "outcome": "passed" }, "teardown": { - "duration": 0.0006222500232979655, + "duration": 0.0002522030845284462, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_image[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", - "lineno": 135, + "lineno": 157, "outcome": "passed", "keywords": [ "test_chat_streaming_image[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", @@ -794,21 +1034,21 @@ "case_id": "case0" }, "setup": { - "duration": 0.016557667055167258, + "duration": 0.07040839456021786, "outcome": "passed" }, "call": { - "duration": 4.930164708988741, + "duration": 4.871666649356484, "outcome": "passed" }, "teardown": { - "duration": 0.00048687495291233063, + "duration": 0.0002490682527422905, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[accounts/fireworks/models/llama-v3p3-70b-instruct-calendar]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[accounts/fireworks/models/llama-v3p3-70b-instruct-calendar]", @@ -827,21 +1067,21 @@ "case_id": "calendar" }, "setup": { - "duration": 0.00886166701093316, + "duration": 0.07167178671807051, "outcome": "passed" }, "call": { - "duration": 0.8833738330285996, + "duration": 0.9903911761939526, "outcome": "passed" }, "teardown": { - "duration": 0.00025583396200090647, + "duration": 0.0002704570069909096, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[accounts/fireworks/models/llama-v3p3-70b-instruct-math]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[accounts/fireworks/models/llama-v3p3-70b-instruct-math]", @@ -860,21 +1100,21 @@ "case_id": "math" }, "setup": { - "duration": 0.01297520799562335, + "duration": 0.07073096185922623, "outcome": "passed" }, "call": { - "duration": 1.9960687910206616, + "duration": 3.9858130905777216, "outcome": "passed" }, "teardown": { - "duration": 0.0005048330640420318, + "duration": 0.00024665892124176025, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[accounts/fireworks/models/llama4-scout-instruct-basic-calendar]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[accounts/fireworks/models/llama4-scout-instruct-basic-calendar]", @@ -893,21 +1133,21 @@ "case_id": "calendar" }, "setup": { - "duration": 0.007275875075720251, + "duration": 0.07138721086084843, "outcome": "passed" }, "call": { - "duration": 0.9094266659813002, + "duration": 1.1312237158417702, "outcome": "passed" }, "teardown": { - "duration": 0.00028041598852723837, + "duration": 0.00027671270072460175, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[accounts/fireworks/models/llama4-scout-instruct-basic-math]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[accounts/fireworks/models/llama4-scout-instruct-basic-math]", @@ -926,21 +1166,21 @@ "case_id": "math" }, "setup": { - "duration": 0.008899332955479622, + "duration": 0.08204951789230108, "outcome": "passed" }, "call": { - "duration": 3.117967874975875, + "duration": 2.7500197598710656, "outcome": "passed" }, "teardown": { - "duration": 0.00017600005958229303, + "duration": 0.00024303700774908066, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[accounts/fireworks/models/llama4-maverick-instruct-basic-calendar]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[accounts/fireworks/models/llama4-maverick-instruct-basic-calendar]", @@ -959,21 +1199,21 @@ "case_id": "calendar" }, "setup": { - "duration": 0.0073364999843761325, + "duration": 0.07405088562518358, "outcome": "passed" }, "call": { - "duration": 2.2714374579954892, + "duration": 1.238045932725072, "outcome": "passed" }, "teardown": { - "duration": 0.0001814159331843257, + "duration": 0.00024984683841466904, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[accounts/fireworks/models/llama4-maverick-instruct-basic-math]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[accounts/fireworks/models/llama4-maverick-instruct-basic-math]", @@ -992,21 +1232,21 @@ "case_id": "math" }, "setup": { - "duration": 0.010546459001488984, + "duration": 0.07009329181164503, "outcome": "passed" }, "call": { - "duration": 3.9954450000077486, + "duration": 3.55908961314708, "outcome": "passed" }, "teardown": { - "duration": 0.0002719159238040447, + "duration": 0.00026627909392118454, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[accounts/fireworks/models/llama-v3p3-70b-instruct-calendar]", - "lineno": 182, + "lineno": 204, "outcome": "passed", "keywords": [ "test_chat_streaming_structured_output[accounts/fireworks/models/llama-v3p3-70b-instruct-calendar]", @@ -1025,21 +1265,21 @@ "case_id": "calendar" }, "setup": { - "duration": 0.012508000014349818, + "duration": 0.07596437353640795, "outcome": "passed" }, "call": { - "duration": 9.095425167004578, + "duration": 1.0093460381031036, "outcome": "passed" }, "teardown": { - "duration": 0.00029200001154094934, + "duration": 0.0002171723172068596, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[accounts/fireworks/models/llama-v3p3-70b-instruct-math]", - "lineno": 182, + "lineno": 204, "outcome": "passed", "keywords": [ "test_chat_streaming_structured_output[accounts/fireworks/models/llama-v3p3-70b-instruct-math]", @@ -1058,21 +1298,21 @@ "case_id": "math" }, "setup": { - "duration": 0.014769250061362982, + "duration": 0.06995268166065216, "outcome": "passed" }, "call": { - "duration": 1.9875252910424024, + "duration": 2.617857910692692, "outcome": "passed" }, "teardown": { - "duration": 0.0006288329605013132, + "duration": 0.00024063047021627426, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[accounts/fireworks/models/llama4-scout-instruct-basic-calendar]", - "lineno": 182, + "lineno": 204, "outcome": "passed", "keywords": [ "test_chat_streaming_structured_output[accounts/fireworks/models/llama4-scout-instruct-basic-calendar]", @@ -1091,21 +1331,21 @@ "case_id": "calendar" }, "setup": { - "duration": 0.014440709026530385, + "duration": 0.0729895168915391, "outcome": "passed" }, "call": { - "duration": 1.2613736250204965, + "duration": 0.9500969992950559, "outcome": "passed" }, "teardown": { - "duration": 0.0001937919296324253, + "duration": 0.000257221981883049, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[accounts/fireworks/models/llama4-scout-instruct-basic-math]", - "lineno": 182, + "lineno": 204, "outcome": "passed", "keywords": [ "test_chat_streaming_structured_output[accounts/fireworks/models/llama4-scout-instruct-basic-math]", @@ -1124,21 +1364,21 @@ "case_id": "math" }, "setup": { - "duration": 0.0071510839043185115, + "duration": 0.07070339564234018, "outcome": "passed" }, "call": { - "duration": 2.2953888749470934, + "duration": 2.6405998673290014, "outcome": "passed" }, "teardown": { - "duration": 0.00016245793085545301, + "duration": 0.0002397783100605011, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[accounts/fireworks/models/llama4-maverick-instruct-basic-calendar]", - "lineno": 182, + "lineno": 204, "outcome": "passed", "keywords": [ "test_chat_streaming_structured_output[accounts/fireworks/models/llama4-maverick-instruct-basic-calendar]", @@ -1157,21 +1397,21 @@ "case_id": "calendar" }, "setup": { - "duration": 0.007294666953384876, + "duration": 0.07140882592648268, "outcome": "passed" }, "call": { - "duration": 2.194703874993138, + "duration": 0.7515814090147614, "outcome": "passed" }, "teardown": { - "duration": 0.00017604196909815073, + "duration": 0.0002773841843008995, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[accounts/fireworks/models/llama4-maverick-instruct-basic-math]", - "lineno": 182, + "lineno": 204, "outcome": "passed", "keywords": [ "test_chat_streaming_structured_output[accounts/fireworks/models/llama4-maverick-instruct-basic-math]", @@ -1190,21 +1430,21 @@ "case_id": "math" }, "setup": { - "duration": 0.019950625021010637, + "duration": 0.07105506956577301, "outcome": "passed" }, "call": { - "duration": 8.4994609169662, + "duration": 3.091084435582161, "outcome": "passed" }, "teardown": { - "duration": 0.00026404205709695816, + "duration": 0.0002588946372270584, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", - "lineno": 204, + "lineno": 226, "outcome": "failed", "keywords": [ "test_chat_non_streaming_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", @@ -1223,34 +1463,34 @@ "case_id": "case0" }, "setup": { - "duration": 0.011928000021725893, + "duration": 0.07215945608913898, "outcome": "passed" }, "call": { - "duration": 0.5664792089955881, + "duration": 1.13668860681355, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 223, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 245, "message": "TypeError: object of type 'NoneType' has no len()" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 223, + "lineno": 245, "message": "TypeError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert len(response.choices[0].message.tool_calls) > 0\nE TypeError: object of type 'NoneType' has no len()\n\ntests/verifications/openai_api/test_chat_completion.py:223: TypeError" + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert len(response.choices[0].message.tool_calls) > 0\nE TypeError: object of type 'NoneType' has no len()\n\ntests/verifications/openai_api/test_chat_completion.py:245: TypeError" }, "teardown": { - "duration": 0.00023799994960427284, + "duration": 0.0003727646544575691, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", - "lineno": 204, + "lineno": 226, "outcome": "failed", "keywords": [ "test_chat_non_streaming_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", @@ -1269,34 +1509,34 @@ "case_id": "case0" }, "setup": { - "duration": 0.006813624990172684, + "duration": 0.07085339725017548, "outcome": "passed" }, "call": { - "duration": 3.170418416033499, + "duration": 6.564900263212621, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 223, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 245, "message": "TypeError: object of type 'NoneType' has no len()" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 223, + "lineno": 245, "message": "TypeError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert len(response.choices[0].message.tool_calls) > 0\nE TypeError: object of type 'NoneType' has no len()\n\ntests/verifications/openai_api/test_chat_completion.py:223: TypeError" + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert len(response.choices[0].message.tool_calls) > 0\nE TypeError: object of type 'NoneType' has no len()\n\ntests/verifications/openai_api/test_chat_completion.py:245: TypeError" }, "teardown": { - "duration": 0.0004129580920562148, + "duration": 0.00036074407398700714, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", - "lineno": 204, + "lineno": 226, "outcome": "failed", "keywords": [ "test_chat_non_streaming_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", @@ -1315,34 +1555,34 @@ "case_id": "case0" }, "setup": { - "duration": 0.01656208303757012, + "duration": 0.07105840742588043, "outcome": "passed" }, "call": { - "duration": 22.76337137504015, + "duration": 1.9664474660530686, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 223, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 245, "message": "TypeError: object of type 'NoneType' has no len()" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 223, + "lineno": 245, "message": "TypeError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert len(response.choices[0].message.tool_calls) > 0\nE TypeError: object of type 'NoneType' has no len()\n\ntests/verifications/openai_api/test_chat_completion.py:223: TypeError" + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert len(response.choices[0].message.tool_calls) > 0\nE TypeError: object of type 'NoneType' has no len()\n\ntests/verifications/openai_api/test_chat_completion.py:245: TypeError" }, "teardown": { - "duration": 0.00038704206235706806, + "duration": 0.0003125220537185669, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", - "lineno": 228, + "lineno": 250, "outcome": "failed", "keywords": [ "test_chat_streaming_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", @@ -1361,34 +1601,34 @@ "case_id": "case0" }, "setup": { - "duration": 0.015727541991509497, + "duration": 0.07491886802017689, "outcome": "passed" }, "call": { - "duration": 0.5719050420448184, + "duration": 1.6239055208861828, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 274, - "message": "assert 0 == 1\n + where 0 = len({})" + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 269, + "message": "assert 0 == 1\n + where 0 = len([])" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 274, + "lineno": 269, "message": "AssertionError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n # Accumulate partial tool_calls here\n tool_calls_buffer = {}\n current_id = None\n # Process streaming chunks\n for chunk in stream:\n choice = chunk.choices[0]\n delta = choice.delta\n \n if delta.tool_calls is None:\n continue\n \n for tool_call_delta in delta.tool_calls:\n if tool_call_delta.id:\n current_id = tool_call_delta.id\n call_id = current_id\n func_delta = tool_call_delta.function\n \n if call_id not in tool_calls_buffer:\n tool_calls_buffer[call_id] = {\n \"id\": call_id,\n \"type\": tool_call_delta.type,\n \"name\": func_delta.name,\n \"arguments\": \"\",\n }\n \n if func_delta.arguments:\n tool_calls_buffer[call_id][\"arguments\"] += func_delta.arguments\n \n> assert len(tool_calls_buffer) == 1\nE assert 0 == 1\nE + where 0 = len({})\n\ntests/verifications/openai_api/test_chat_completion.py:274: AssertionError" + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n _, tool_calls_buffer = _accumulate_streaming_tool_calls(stream)\n> assert len(tool_calls_buffer) == 1\nE assert 0 == 1\nE + where 0 = len([])\n\ntests/verifications/openai_api/test_chat_completion.py:269: AssertionError" }, "teardown": { - "duration": 0.0003532909322530031, + "duration": 0.0003996873274445534, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", - "lineno": 228, + "lineno": 250, "outcome": "failed", "keywords": [ "test_chat_streaming_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", @@ -1407,34 +1647,34 @@ "case_id": "case0" }, "setup": { - "duration": 0.011914041941054165, + "duration": 0.07084537390619516, "outcome": "passed" }, "call": { - "duration": 5.403063916950487, + "duration": 7.175910825841129, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 274, - "message": "assert 0 == 1\n + where 0 = len({})" + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 269, + "message": "assert 0 == 1\n + where 0 = len([])" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 274, + "lineno": 269, "message": "AssertionError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n # Accumulate partial tool_calls here\n tool_calls_buffer = {}\n current_id = None\n # Process streaming chunks\n for chunk in stream:\n choice = chunk.choices[0]\n delta = choice.delta\n \n if delta.tool_calls is None:\n continue\n \n for tool_call_delta in delta.tool_calls:\n if tool_call_delta.id:\n current_id = tool_call_delta.id\n call_id = current_id\n func_delta = tool_call_delta.function\n \n if call_id not in tool_calls_buffer:\n tool_calls_buffer[call_id] = {\n \"id\": call_id,\n \"type\": tool_call_delta.type,\n \"name\": func_delta.name,\n \"arguments\": \"\",\n }\n \n if func_delta.arguments:\n tool_calls_buffer[call_id][\"arguments\"] += func_delta.arguments\n \n> assert len(tool_calls_buffer) == 1\nE assert 0 == 1\nE + where 0 = len({})\n\ntests/verifications/openai_api/test_chat_completion.py:274: AssertionError" + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n _, tool_calls_buffer = _accumulate_streaming_tool_calls(stream)\n> assert len(tool_calls_buffer) == 1\nE assert 0 == 1\nE + where 0 = len([])\n\ntests/verifications/openai_api/test_chat_completion.py:269: AssertionError" }, "teardown": { - "duration": 0.0005193749675527215, + "duration": 0.0003013862296938896, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", - "lineno": 228, + "lineno": 250, "outcome": "failed", "keywords": [ "test_chat_streaming_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", @@ -1453,31 +1693,2059 @@ "case_id": "case0" }, "setup": { - "duration": 0.012608832912519574, + "duration": 0.07152015157043934, "outcome": "passed" }, "call": { - "duration": 7.587262416025624, + "duration": 9.749054622836411, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 274, - "message": "assert 0 == 1\n + where 0 = len({})" + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 269, + "message": "assert 0 == 1\n + where 0 = len([])" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 274, + "lineno": 269, "message": "AssertionError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n # Accumulate partial tool_calls here\n tool_calls_buffer = {}\n current_id = None\n # Process streaming chunks\n for chunk in stream:\n choice = chunk.choices[0]\n delta = choice.delta\n \n if delta.tool_calls is None:\n continue\n \n for tool_call_delta in delta.tool_calls:\n if tool_call_delta.id:\n current_id = tool_call_delta.id\n call_id = current_id\n func_delta = tool_call_delta.function\n \n if call_id not in tool_calls_buffer:\n tool_calls_buffer[call_id] = {\n \"id\": call_id,\n \"type\": tool_call_delta.type,\n \"name\": func_delta.name,\n \"arguments\": \"\",\n }\n \n if func_delta.arguments:\n tool_calls_buffer[call_id][\"arguments\"] += func_delta.arguments\n \n> assert len(tool_calls_buffer) == 1\nE assert 0 == 1\nE + where 0 = len({})\n\ntests/verifications/openai_api/test_chat_completion.py:274: AssertionError" + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n _, tool_calls_buffer = _accumulate_streaming_tool_calls(stream)\n> assert len(tool_calls_buffer) == 1\nE assert 0 == 1\nE + where 0 = len([])\n\ntests/verifications/openai_api/test_chat_completion.py:269: AssertionError" }, "teardown": { - "duration": 0.0008685829816386104, + "duration": 0.0002990690991282463, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_required[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", + "lineno": 278, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_tool_choice_required[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "case0" + }, + "setup": { + "duration": 0.07075500208884478, + "outcome": "passed" + }, + "call": { + "duration": 0.9870151281356812, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00022785458713769913, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_required[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", + "lineno": 278, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_tool_choice_required[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "case0" + }, + "setup": { + "duration": 0.0698307491838932, + "outcome": "passed" + }, + "call": { + "duration": 4.061793921515346, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 298, + "message": "TypeError: object of type 'NoneType' has no len()" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 298, + "message": "TypeError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_choice_required(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"required\", # Force tool call\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert len(response.choices[0].message.tool_calls) > 0, \"Expected tool call when tool_choice='required'\"\nE TypeError: object of type 'NoneType' has no len()\n\ntests/verifications/openai_api/test_chat_completion.py:298: TypeError" + }, + "teardown": { + "duration": 0.00028742197901010513, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_required[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", + "lineno": 278, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_tool_choice_required[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "case0" + }, + "setup": { + "duration": 0.07069965451955795, + "outcome": "passed" + }, + "call": { + "duration": 24.973835667595267, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 298, + "message": "TypeError: object of type 'NoneType' has no len()" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 298, + "message": "TypeError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_choice_required(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"required\", # Force tool call\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert len(response.choices[0].message.tool_calls) > 0, \"Expected tool call when tool_choice='required'\"\nE TypeError: object of type 'NoneType' has no len()\n\ntests/verifications/openai_api/test_chat_completion.py:298: TypeError" + }, + "teardown": { + "duration": 0.00034868158400058746, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_required[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", + "lineno": 302, + "outcome": "passed", + "keywords": [ + "test_chat_streaming_tool_choice_required[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "case0" + }, + "setup": { + "duration": 0.07031871005892754, + "outcome": "passed" + }, + "call": { + "duration": 0.7874777475371957, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00027067307382822037, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_required[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", + "lineno": 302, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_tool_choice_required[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "case0" + }, + "setup": { + "duration": 0.07194838207215071, + "outcome": "passed" + }, + "call": { + "duration": 5.034253670834005, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 323, + "message": "AssertionError: Expected tool call when tool_choice='required'\nassert 0 > 0\n + where 0 = len([])" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 323, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_choice_required(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"required\", # Force tool call\n stream=True,\n )\n \n _, tool_calls_buffer = _accumulate_streaming_tool_calls(stream)\n \n> assert len(tool_calls_buffer) > 0, \"Expected tool call when tool_choice='required'\"\nE AssertionError: Expected tool call when tool_choice='required'\nE assert 0 > 0\nE + where 0 = len([])\n\ntests/verifications/openai_api/test_chat_completion.py:323: AssertionError" + }, + "teardown": { + "duration": 0.00030618347227573395, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_required[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", + "lineno": 302, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_tool_choice_required[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "case0" + }, + "setup": { + "duration": 0.07107715681195259, + "outcome": "passed" + }, + "call": { + "duration": 6.841737313196063, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 323, + "message": "AssertionError: Expected tool call when tool_choice='required'\nassert 0 > 0\n + where 0 = len([])" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 323, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_choice_required(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"required\", # Force tool call\n stream=True,\n )\n \n _, tool_calls_buffer = _accumulate_streaming_tool_calls(stream)\n \n> assert len(tool_calls_buffer) > 0, \"Expected tool call when tool_choice='required'\"\nE AssertionError: Expected tool call when tool_choice='required'\nE assert 0 > 0\nE + where 0 = len([])\n\ntests/verifications/openai_api/test_chat_completion.py:323: AssertionError" + }, + "teardown": { + "duration": 0.0003354279324412346, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_none[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", + "lineno": 329, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_tool_choice_none[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "case0" + }, + "setup": { + "duration": 0.0726231737062335, + "outcome": "passed" + }, + "call": { + "duration": 0.7659661257639527, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0003337552770972252, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_none[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", + "lineno": 329, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_tool_choice_none[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "case0" + }, + "setup": { + "duration": 0.09297824744135141, + "outcome": "passed" + }, + "call": { + "duration": 3.257608976215124, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00022768322378396988, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_none[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", + "lineno": 329, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_tool_choice_none[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "case0" + }, + "setup": { + "duration": 0.0726541867479682, + "outcome": "passed" + }, + "call": { + "duration": 4.5413802824914455, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00026340410113334656, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_none[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", + "lineno": 352, + "outcome": "passed", + "keywords": [ + "test_chat_streaming_tool_choice_none[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "case0" + }, + "setup": { + "duration": 0.07666508108377457, + "outcome": "passed" + }, + "call": { + "duration": 0.5535151390358806, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0003251638263463974, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_none[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", + "lineno": 352, + "outcome": "passed", + "keywords": [ + "test_chat_streaming_tool_choice_none[accounts/fireworks/models/llama4-scout-instruct-basic-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "case0" + }, + "setup": { + "duration": 0.09550460614264011, + "outcome": "passed" + }, + "call": { + "duration": 1.171110725030303, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0002604629844427109, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_none[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", + "lineno": 352, + "outcome": "passed", + "keywords": [ + "test_chat_streaming_tool_choice_none[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "case0" + }, + "setup": { + "duration": 0.07114547491073608, + "outcome": "passed" + }, + "call": { + "duration": 27.369331603869796, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00023956969380378723, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-text_then_weather_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-text_then_weather_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07612851448357105, + "outcome": "passed" + }, + "call": { + "duration": 2.10164753254503, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 467, + "message": "AssertionError: Expected one of ['sol'] in content, but got: 'I cannot perform this task as it requires additional functionality that is not available in the given functions.'\nassert False\n + where False = any(. at 0x7f1acda87ca0>)" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 467, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n tool_call = assistant_message.tool_calls[0]\n assert tool_call.function.name == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call.function.name}'\"\n )\n # Parse the JSON string arguments before comparing\n actual_arguments = json.loads(tool_call.function.arguments)\n assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\n \n # Prepare and append the tool response for the next turn\n tool_response = tool_responses.pop(0)\n messages.append(\n {\n \"role\": \"tool\",\n \"tool_call_id\": tool_call.id,\n \"content\": tool_response[\"response\"],\n }\n )\n else:\n assert assistant_message.content is not None, \"Expected content, but none received.\"\n expected_answers = expected[\"answer\"] # This is now a list\n content_lower = assistant_message.content.lower()\n> assert any(ans.lower() in content_lower for ans in expected_answers), (\n f\"Expected one of {expected_answers} in content, but got: '{assistant_message.content}'\"\n )\nE AssertionError: Expected one of ['sol'] in content, but got: 'I cannot perform this task as it requires additional functionality that is not available in the given functions.'\nE assert False\nE + where False = any(. at 0x7f1acda87ca0>)\n\ntests/verifications/openai_api/test_chat_completion.py:467: AssertionError" + }, + "teardown": { + "duration": 0.00030514132231473923, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-weather_tool_then_text]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-weather_tool_then_text]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.07009781803935766, + "outcome": "passed" + }, + "call": { + "duration": 2.49614445772022, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"type\": \"function\", \"name\": \"get_weather\", \"parameters\": {\"location\": \"San Francisco, CA\"}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'weather_tool_then_text', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'location': 'San Francisco...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"type\": \"function\", \"name\": \"get_weather\", \"parameters\": {\"location\": \"San Francisco, CA\"}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.00035297591239213943, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-add_product_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-add_product_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.0719120567664504, + "outcome": "passed" + }, + "call": { + "duration": 1.181352874264121, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"type\": \"function\", \"name\": \"addProduct\", \"parameters\": {\"name\": \"Widget\", \"price\": \"19.99\", \"inStock\": \"true\", \"tags\": \"[\\\\\"new\\\\\", \\\\\"sale\\\\\"]\"}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'add_product_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'inStock': True, 'name': 'Widget...}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': 'Successfully added product with id: 123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"type\": \"function\", \"name\": \"addProduct\", \"parameters\": {\"name\": \"Widget\", \"price\": \"19.99\", \"inStock\": \"true\", \"tags\": \"[\\\\\"new\\\\\", \\\\\"sale\\\\\"]\"}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.000303901731967926, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-get_then_create_event_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.07158921286463737, + "outcome": "passed" + }, + "call": { + "duration": 3.7202864307910204, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'get_then_create_event_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'date': '2025-03-03', ...ents found for 2025-03-03 at 10:00'}\"}, {'response': \"{'response': 'Successfully created new event with id: e_123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.0003700554370880127, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-compare_monthly_expense_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.07388217654079199, + "outcome": "passed" + }, + "call": { + "duration": 0.6030126195400953, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"type\": \"function\", \"name\": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": \"1\", \"year\": \"2025\"}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'compare_monthly_expense_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'month': 1, 'year': ... 'Total expenses for January 2025: $1000'}\"}, {'response': \"{'response': 'Total expenses for February 2024: $2000'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"type\": \"function\", \"name\": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": \"1\", \"year\": \"2025\"}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.0003188345581293106, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-text_then_weather_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-text_then_weather_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07314795535057783, + "outcome": "passed" + }, + "call": { + "duration": 1.0849075820297003, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 467, + "message": "AssertionError: Expected one of ['sol'] in content, but got: '{\"name\": \"get_weather\", \"parameters\": {\"description\": \"Get the current weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"description\": \"The city and state (both required). e.g. San Francisco, CA.\", \"type\": \"string\"}}}}'\nassert False\n + where False = any(. at 0x7f1acdad8970>)" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 467, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n tool_call = assistant_message.tool_calls[0]\n assert tool_call.function.name == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call.function.name}'\"\n )\n # Parse the JSON string arguments before comparing\n actual_arguments = json.loads(tool_call.function.arguments)\n assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\n \n # Prepare and append the tool response for the next turn\n tool_response = tool_responses.pop(0)\n messages.append(\n {\n \"role\": \"tool\",\n \"tool_call_id\": tool_call.id,\n \"content\": tool_response[\"response\"],\n }\n )\n else:\n assert assistant_message.content is not None, \"Expected content, but none received.\"\n expected_answers = expected[\"answer\"] # This is now a list\n content_lower = assistant_message.content.lower()\n> assert any(ans.lower() in content_lower for ans in expected_answers), (\n f\"Expected one of {expected_answers} in content, but got: '{assistant_message.content}'\"\n )\nE AssertionError: Expected one of ['sol'] in content, but got: '{\"name\": \"get_weather\", \"parameters\": {\"description\": \"Get the current weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"description\": \"The city and state (both required). e.g. San Francisco, CA.\", \"type\": \"string\"}}}}'\nE assert False\nE + where False = any(. at 0x7f1acdad8970>)\n\ntests/verifications/openai_api/test_chat_completion.py:467: AssertionError" + }, + "teardown": { + "duration": 0.00032442156225442886, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-weather_tool_then_text]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-weather_tool_then_text]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.07257637288421392, + "outcome": "passed" + }, + "call": { + "duration": 1.1364115234464407, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"name\": \"get_weather\", \"parameters\": {\"description\": \"Get the current weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"description\": \"The city and state (both required)\", \"type\": \"string\"}}}, \"required\": [\"location\"]}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'weather_tool_then_text', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'location': 'San Francisco...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"name\": \"get_weather\", \"parameters\": {\"description\": \"Get the current weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"description\": \"The city and state (both required)\", \"type\": \"string\"}}}, \"required\": [\"location\"]}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.0003107702359557152, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-add_product_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-add_product_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.0716616166755557, + "outcome": "passed" + }, + "call": { + "duration": 1.6755285635590553, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"name\": \"addProduct\", \"parameters\": {\"name\": {\"type\": \"string\", \"value\": \"Widget\"}, \"description\": {\"type\": \"string\", \"value\": \"Name of the product\"}, \"price\": {\"type\": \"number\", \"value\": 19.99}, \"inStock\": {\"type\": \"boolean\", \"value\": true}, \"tags\": {\"type\": \"array\", \"value\": [\"new\", \"sale\"]}}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'add_product_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'inStock': True, 'name': 'Widget...}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': 'Successfully added product with id: 123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"name\": \"addProduct\", \"parameters\": {\"name\": {\"type\": \"string\", \"value\": \"Widget\"}, \"description\": {\"type\": \"string\", \"value\": \"Name of the product\"}, \"price\": {\"type\": \"number\", \"value\": 19.99}, \"inStock\": {\"type\": \"boolean\", \"value\": true}, \"tags\": {\"type\": \"array\", \"value\": [\"new\", \"sale\"]}}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.0003323536366224289, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-get_then_create_event_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.07031949236989021, + "outcome": "passed" + }, + "call": { + "duration": 2.363899651914835, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"name\": \"get_event\", \"parameters\": {\"date\": {\"date\": \"March 3rd\"}, \"time\": {\"time\": \"10 am\"}}}assistant\\n\\n{\"name\": \"get_event\", \"parameters\": {\"date\": {\"date\": \"2025-03-03\"}, \"time\": {\"time\": \"10:00\"}}}assistant\\n\\n{\"name\": \"get_event\", \"parameters\": {\"date\": {\"date\": \"2025-03-03\"}, \"time\": {\"time\": \"10:00\"}}}assistant\\n\\n{\"name\": \"get_event\", \"parameters\": {\"date\": {\"date\": \"2025-03-03\"}, \"time\": {\"time\": \"10:00\"}}}assistant\\n\\nThe function provided is not sufficient for me to answer the question.assistant\\n\\n{\"name\": \"get_event\", \"parameters\": {\"date\": {\"date\": \"2025-03-03\"}, \"time\": {\"time\": \"10:00\"}}}assistant\\n\\nThe function provided is not sufficient for me to answer the question.', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'get_then_create_event_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'date': '2025-03-03', ...ents found for 2025-03-03 at 10:00'}\"}, {'response': \"{'response': 'Successfully created new event with id: e_123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"name\": \"get_event\", \"parameters\": {\"date\": {\"date\": \"March 3rd\"}, \"time\": {\"time\": \"10 am\"}}}assistant\\n\\n{\"name\": \"get_event\", \"parameters\": {\"date\": {\"date\": \"2025-03-03\"}, \"time\": {\"time\": \"10:00\"}}}assistant\\n\\n{\"name\": \"get_event\", \"parameters\": {\"date\": {\"date\": \"2025-03-03\"}, \"time\": {\"time\": \"10:00\"}}}assistant\\n\\n{\"name\": \"get_event\", \"parameters\": {\"date\": {\"date\": \"2025-03-03\"}, \"time\": {\"time\": \"10:00\"}}}assistant\\n\\nThe function provided is not sufficient for me to answer the question.assistant\\n\\n{\"name\": \"get_event\", \"parameters\": {\"date\": {\"date\": \"2025-03-03\"}, \"time\": {\"time\": \"10:00\"}}}assistant\\n\\nThe function provided is not sufficient for me to answer the question.', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.0003245687112212181, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-compare_monthly_expense_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.07069017831236124, + "outcome": "passed" + }, + "call": { + "duration": 1.8757586162537336, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"name\": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": {\"description\": \"Month of the year (1-12)\", \"type\": \"integer\"}, \"year\": {\"description\": \"Year\", \"type\": \"integer\"}}}assistant\\n\\n{\"name\": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": {\"description\": \"Month of the year (1-12)\", \"type\": \"integer\"}, \"year\": {\"description\": \"Year\", \"type\": \"integer\"}}}assistant\\n\\n{\"name\": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": {\"description\": \"Month of the year (1-12)\", \"type\": \"integer\", \"value\": 1}, \"year\": {\"description\": \"Year\", \"type\": \"integer\", \"value\": 2025}}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'compare_monthly_expense_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'month': 1, 'year': ... 'Total expenses for January 2025: $1000'}\"}, {'response': \"{'response': 'Total expenses for February 2024: $2000'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"name\": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": {\"description\": \"Month of the year (1-12)\", \"type\": \"integer\"}, \"year\": {\"description\": \"Year\", \"type\": \"integer\"}}}assistant\\n\\n{\"name\": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": {\"description\": \"Month of the year (1-12)\", \"type\": \"integer\"}, \"year\": {\"description\": \"Year\", \"type\": \"integer\"}}}assistant\\n\\n{\"name\": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": {\"description\": \"Month of the year (1-12)\", \"type\": \"integer\", \"value\": 1}, \"year\": {\"description\": \"Year\", \"type\": \"integer\", \"value\": 2025}}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.00030215736478567123, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-text_then_weather_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-text_then_weather_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07024750486016273, + "outcome": "passed" + }, + "call": { + "duration": 2.9532439298927784, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 467, + "message": "AssertionError: Expected one of ['sol'] in content, but got: 'Since there's no function defined to directly answer \"What's the name of the Sun in latin?\", I'll assume there's a general knowledge or information retrieval function available. Let's call it \"get_general_knowledge\". \n \n Here is a potential JSON response for a function call:\n \n {\"name\": \"get_general_knowledge\", \"parameters\": {\"query\": \"Latin name of the Sun\"}} \n \n However, the exact function and parameter names might vary based on the actual function definitions available. If we consider the given function \"get_weather\" and its parameters, it doesn't fit the prompt. Therefore, based on a hypothetical \"get_general_knowledge\" function, the response is provided. \n \n If the actual available functions were listed, a more accurate response could be provided. \n \n For the sake of the given prompt and assuming the presence of a \"get_general_knowledge\" function, the response is:\n \n {\"name\": \"get_general_knowledge\", \"parameters\": {\"query\": \"Latin name of the Sun\"}}'\nassert False\n + where False = any(. at 0x7f1acd9d54d0>)" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 467, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n tool_call = assistant_message.tool_calls[0]\n assert tool_call.function.name == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call.function.name}'\"\n )\n # Parse the JSON string arguments before comparing\n actual_arguments = json.loads(tool_call.function.arguments)\n assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\n \n # Prepare and append the tool response for the next turn\n tool_response = tool_responses.pop(0)\n messages.append(\n {\n \"role\": \"tool\",\n \"tool_call_id\": tool_call.id,\n \"content\": tool_response[\"response\"],\n }\n )\n else:\n assert assistant_message.content is not None, \"Expected content, but none received.\"\n expected_answers = expected[\"answer\"] # This is now a list\n content_lower = assistant_message.content.lower()\n> assert any(ans.lower() in content_lower for ans in expected_answers), (\n f\"Expected one of {expected_answers} in content, but got: '{assistant_message.content}'\"\n )\nE AssertionError: Expected one of ['sol'] in content, but got: 'Since there's no function defined to directly answer \"What's the name of the Sun in latin?\", I'll assume there's a general knowledge or information retrieval function available. Let's call it \"get_general_knowledge\". \nE \nE Here is a potential JSON response for a function call:\nE \nE {\"name\": \"get_general_knowledge\", \"parameters\": {\"query\": \"Latin name of the Sun\"}} \nE \nE However, the exact function and parameter names might vary based on the actual function definitions available. If we consider the given function \"get_weather\" and its parameters, it doesn't fit the prompt. Therefore, based on a hypothetical \"get_general_knowledge\" function, the response is provided. \nE \nE If the actual available functions were listed, a more accurate response could be provided. \nE \nE For the sake of the given prompt and assuming the presence of a \"get_general_knowledge\" function, the response is:\nE \nE {\"name\": \"get_general_knowledge\", \"parameters\": {\"query\": \"Latin name of the Sun\"}}'\nE assert False\nE + where False = any(. at 0x7f1acd9d54d0>)\n\ntests/verifications/openai_api/test_chat_completion.py:467: AssertionError" + }, + "teardown": { + "duration": 0.00038253143429756165, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-weather_tool_then_text]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-weather_tool_then_text]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.07193771284073591, + "outcome": "passed" + }, + "call": { + "duration": 0.9909431086853147, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"name\": \"get_weather\", \"parameters\": {\"location\": \"San Francisco, CA\"}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'weather_tool_then_text', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'location': 'San Francisco...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"name\": \"get_weather\", \"parameters\": {\"location\": \"San Francisco, CA\"}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.0003658318892121315, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-add_product_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-add_product_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.0702557684853673, + "outcome": "passed" + }, + "call": { + "duration": 0.8836336443200707, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"name\": \"addProduct\", \"parameters\": {\"name\": \"Widget\", \"price\": 19.99, \"inStock\": true, \"tags\": [\"new\", \"sale\"]}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'add_product_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'inStock': True, 'name': 'Widget...}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': 'Successfully added product with id: 123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"name\": \"addProduct\", \"parameters\": {\"name\": \"Widget\", \"price\": 19.99, \"inStock\": true, \"tags\": [\"new\", \"sale\"]}}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.00036840979009866714, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-get_then_create_event_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.07019469328224659, + "outcome": "passed" + }, + "call": { + "duration": 7.394101745449007, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}} \"\\n\\n{\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}}assistant\\n\\n{\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}}assistant\\n\\nimport json\\noutput = json.loads(\"{\\\\\"type\\\\\": \\\\\"function\\\\\", \\\\\"name\\\\\": \\\\\"get_event\\\\\", \\\\\"parameters\\\\\": {\\\\\"date\\\\\": \\\\\"2025-03-03\\\\\", \\\\\"time\\\\\": \\\\\"10:00\\\\\"}}\")\\nprint(output)assistant\\n\\nimport json\\n\\n# Define the input data\\ndata = \\'[{\"type\": \"function\", \"name\": \"create_event\", \"parameters\": {\"name\": \"Meeting\", \"date\": \"2025-03-03\", \"time\": \"10:00\", \"location\": \"Conference Room\", \"participants\": [\"John\", \"Jane\"]}}, {\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}}]\\'\\n\\n# Parse the JSON string into a Python object\\njson_data = json.loads(data)\\n\\n# Print the parsed data\\nprint(json_data)assistant\\n\\nimport json\\n\\ndef check_meeting(data):\\n # Parse the JSON string into a Python object\\n json_data = json.loads(data)\\n\\n # Check if there is a meeting on March 3rd at 10 am\\n for item in json_data:\\n if item[\\'type\\'] == \\'function\\' and item[\\'name\\'] == \\'get_event\\':\\n date = item[\\'parameters\\'][\\'date\\']\\n time = item[\\'parameters\\'][\\'time\\']\\n # Assuming you have a function to check if there is a meeting at the given date and time\\n # For simplicity, let\\'s assume the function is called \\'has_meeting\\'\\n # if has_meeting(date, time):\\n # return \\'Yes\\'\\n # else:\\n # return \\'No\\'\\n return \\'Yes\\' # For demonstration purposes, let\\'s assume there is a meeting\\n\\n return \\'No\\'\\n\\ndata = \\'[{\"type\": \"function\", \"name\": \"create_event\", \"parameters\": {\"name\": \"Meeting\", \"date\": \"2025-03-03\", \"time\": \"10:00\", \"location\": \"Conference Room\", \"participants\": [\"John\", \"Jane\"]}}, {\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}}]\\'\\nprint(check_meeting(data))assistant\\n\\nYes.', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'get_then_create_event_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'date': '2025-03-03', ...ents found for 2025-03-03 at 10:00'}\"}, {'response': \"{'response': 'Successfully created new event with id: e_123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}} \"\\n\\n{\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}}assistant\\n\\n{\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}}assistant\\n\\nimport json\\noutput = json.loads(\"{\\\\\"type\\\\\": \\\\\"function\\\\\", \\\\\"name\\\\\": \\\\\"get_event\\\\\", \\\\\"parameters\\\\\": {\\\\\"date\\\\\": \\\\\"2025-03-03\\\\\", \\\\\"time\\\\\": \\\\\"10:00\\\\\"}}\")\\nprint(output)assistant\\n\\nimport json\\n\\n# Define the input data\\ndata = \\'[{\"type\": \"function\", \"name\": \"create_event\", \"parameters\": {\"name\": \"Meeting\", \"date\": \"2025-03-03\", \"time\": \"10:00\", \"location\": \"Conference Room\", \"participants\": [\"John\", \"Jane\"]}}, {\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}}]\\'\\n\\n# Parse the JSON string into a Python object\\njson_data = json.loads(data)\\n\\n# Print the parsed data\\nprint(json_data)assistant\\n\\nimport json\\n\\ndef check_meeting(data):\\n # Parse the JSON string into a Python object\\n json_data = json.loads(data)\\n\\n # Check if there is a meeting on March 3rd at 10 am\\n for item in json_data:\\n if item[\\'type\\'] == \\'function\\' and item[\\'name\\'] == \\'get_event\\':\\n date = item[\\'parameters\\'][\\'date\\']\\n time = item[\\'parameters\\'][\\'time\\']\\n # Assuming you have a function to check if there is a meeting at the given date and time\\n # For simplicity, let\\'s assume the function is called \\'has_meeting\\'\\n # if has_meeting(date, time):\\n # return \\'Yes\\'\\n # else:\\n # return \\'No\\'\\n return \\'Yes\\' # For demonstration purposes, let\\'s assume there is a meeting\\n\\n return \\'No\\'\\n\\ndata = \\'[{\"type\": \"function\", \"name\": \"create_event\", \"parameters\": {\"name\": \"Meeting\", \"date\": \"2025-03-03\", \"time\": \"10:00\", \"location\": \"Conference Room\", \"participants\": [\"John\", \"Jane\"]}}, {\"type\": \"function\", \"name\": \"get_event\", \"parameters\": {\"date\": \"2025-03-03\", \"time\": \"10:00\"}}]\\'\\nprint(check_meeting(data))assistant\\n\\nYes.', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.0003475993871688843, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-compare_monthly_expense_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.07140176557004452, + "outcome": "passed" + }, + "call": { + "duration": 1.5649437978863716, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len((None or []))\n + where None = ChatCompletionMessage(content='{\"name\": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": 1, \"year\": 2024}}\"\" \"\" \" \"\"\"\"\"\"\"\"\"\"\"\"\" \"\" \"\"\" \"}\",\"\" \" \"}\",\"\" \" \"}\",\"\" \" \"{\" \"name\" \": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": 1, \"year\": 2024}}\"', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'compare_monthly_expense_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'month': 1, 'year': ... 'Total expenses for January 2025: $1000'}\"}, {'response': \"{'response': 'Total expenses for February 2024: $2000'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len((None or []))\nE + where None = ChatCompletionMessage(content='{\"name\": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": 1, \"year\": 2024}}\"\" \"\" \" \"\"\"\"\"\"\"\"\"\"\"\"\" \"\" \"\"\" \"}\",\"\" \" \"}\",\"\" \" \"}\",\"\" \" \"{\" \"name\" \": \"getMonthlyExpenseSummary\", \"parameters\": {\"month\": 1, \"year\": 2024}}\"', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.00034684035927057266, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-text_then_weather_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-text_then_weather_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07161083538085222, + "outcome": "passed" + }, + "call": { + "duration": 0.972024847753346, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 550, + "message": "AssertionError: Expected one of ['sol'] in content, but got: 'I cannot perform this task as it requires additional functionality that is not available in the given functions.'\nassert False\n + where False = any(. at 0x7f1acd9d4510>)" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 550, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n # Use the first accumulated tool call for assertion\n tool_call = accumulated_tool_calls[0]\n assert tool_call[\"function\"][\"name\"] == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call['function']['name']}'\"\n )\n # Parse the accumulated arguments string for comparison\n actual_arguments = json.loads(tool_call[\"function\"][\"arguments\"])\n assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\n \n # Prepare and append the tool response for the next turn\n tool_response = tool_responses.pop(0)\n messages.append(\n {\n \"role\": \"tool\",\n \"tool_call_id\": tool_call[\"id\"],\n \"content\": tool_response[\"response\"],\n }\n )\n else:\n assert accumulated_content is not None and accumulated_content != \"\", \"Expected content, but none received.\"\n expected_answers = expected[\"answer\"]\n content_lower = accumulated_content.lower()\n> assert any(ans.lower() in content_lower for ans in expected_answers), (\n f\"Expected one of {expected_answers} in content, but got: '{accumulated_content}'\"\n )\nE AssertionError: Expected one of ['sol'] in content, but got: 'I cannot perform this task as it requires additional functionality that is not available in the given functions.'\nE assert False\nE + where False = any(. at 0x7f1acd9d4510>)\n\ntests/verifications/openai_api/test_chat_completion.py:550: AssertionError" + }, + "teardown": { + "duration": 0.0003080591559410095, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-weather_tool_then_text]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-weather_tool_then_text]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.07267874106764793, + "outcome": "passed" + }, + "call": { + "duration": 0.632216920144856, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'weather_tool_then_text', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'location': 'San Francisco...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.0003350367769598961, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-add_product_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-add_product_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.0707720061764121, + "outcome": "passed" + }, + "call": { + "duration": 0.9429405080154538, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'add_product_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'inStock': True, 'name': 'Widget...}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': 'Successfully added product with id: 123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.0002858620136976242, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-get_then_create_event_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.06923680566251278, + "outcome": "passed" + }, + "call": { + "duration": 0.7107308339327574, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'get_then_create_event_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'date': '2025-03-03', ...ents found for 2025-03-03 at 10:00'}\"}, {'response': \"{'response': 'Successfully created new event with id: e_123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.0003181472420692444, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-compare_monthly_expense_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.07021687645465136, + "outcome": "passed" + }, + "call": { + "duration": 0.7717038569971919, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'compare_monthly_expense_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'month': 1, 'year': ... 'Total expenses for January 2025: $1000'}\"}, {'response': \"{'response': 'Total expenses for February 2024: $2000'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.00030398648232221603, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-text_then_weather_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-text_then_weather_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07320436742156744, + "outcome": "passed" + }, + "call": { + "duration": 1.2869794629514217, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 550, + "message": "AssertionError: Expected one of ['sol'] in content, but got: '{\"name\": \"get_weather\", \"parameters\": {\"description\": \"Get the current weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"description\": \"The city and state (both required) (e.g. San Francisco, CA.\", \"type\": \"string\"}}}, \"required\": [\"location\"]}}'\nassert False\n + where False = any(. at 0x7f1acd9b8e40>)" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 550, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n # Use the first accumulated tool call for assertion\n tool_call = accumulated_tool_calls[0]\n assert tool_call[\"function\"][\"name\"] == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call['function']['name']}'\"\n )\n # Parse the accumulated arguments string for comparison\n actual_arguments = json.loads(tool_call[\"function\"][\"arguments\"])\n assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\n \n # Prepare and append the tool response for the next turn\n tool_response = tool_responses.pop(0)\n messages.append(\n {\n \"role\": \"tool\",\n \"tool_call_id\": tool_call[\"id\"],\n \"content\": tool_response[\"response\"],\n }\n )\n else:\n assert accumulated_content is not None and accumulated_content != \"\", \"Expected content, but none received.\"\n expected_answers = expected[\"answer\"]\n content_lower = accumulated_content.lower()\n> assert any(ans.lower() in content_lower for ans in expected_answers), (\n f\"Expected one of {expected_answers} in content, but got: '{accumulated_content}'\"\n )\nE AssertionError: Expected one of ['sol'] in content, but got: '{\"name\": \"get_weather\", \"parameters\": {\"description\": \"Get the current weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"description\": \"The city and state (both required) (e.g. San Francisco, CA.\", \"type\": \"string\"}}}, \"required\": [\"location\"]}}'\nE assert False\nE + where False = any(. at 0x7f1acd9b8e40>)\n\ntests/verifications/openai_api/test_chat_completion.py:550: AssertionError" + }, + "teardown": { + "duration": 0.0003076540306210518, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-weather_tool_then_text]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-weather_tool_then_text]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.0732570867985487, + "outcome": "passed" + }, + "call": { + "duration": 0.9204158475622535, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'weather_tool_then_text', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'location': 'San Francisco...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.000310627743601799, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-add_product_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-add_product_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.07232664246112108, + "outcome": "passed" + }, + "call": { + "duration": 3.829266043379903, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'add_product_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'inStock': True, 'name': 'Widget...}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': 'Successfully added product with id: 123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.00034091807901859283, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-get_then_create_event_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.07045515719801188, + "outcome": "passed" + }, + "call": { + "duration": 6.550140863284469, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'get_then_create_event_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'date': '2025-03-03', ...ents found for 2025-03-03 at 10:00'}\"}, {'response': \"{'response': 'Successfully created new event with id: e_123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.0003092316910624504, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-compare_monthly_expense_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-scout-instruct-basic-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-scout-instruct-basic", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.07400601450353861, + "outcome": "passed" + }, + "call": { + "duration": 3.142588397487998, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'compare_monthly_expense_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'month': 1, 'year': ... 'Total expenses for January 2025: $1000'}\"}, {'response': \"{'response': 'Total expenses for February 2024: $2000'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.0003124792128801346, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-text_then_weather_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-text_then_weather_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07049713470041752, + "outcome": "passed" + }, + "call": { + "duration": 4.074657499790192, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 550, + "message": "AssertionError: Expected one of ['sol'] in content, but got: 'Since the provided text describes a JSON schema for a function call to get the weather, and the prompt asks for the name of the Sun in Latin, we need to identify a suitable function that can provide this information. However, the given schema is for a \"get_weather\" function, which doesn't directly relate to the question about the Sun's name in Latin.\n \n Assuming there's another function available that can provide information about celestial bodies or their names in different languages, we might look for something like \"get_celestial_body_info\" or a similar function.\n \n However, based on the given format and the information provided, it seems there's an implication that we should directly provide a response in the specified JSON format for a hypothetical or related function. Let's assume a function named \"get_celestial_body_name\" that takes parameters like \"body\" and \"language\".\n \n Given the constraint of the format and assuming a function that fits, we might construct a response like:\n \n ```json\n {\n \"name\": \"get_celestial_body_name\",\n \"parameters\": {\n \"body\": \"Sun\",\n \"language\": \"Latin\"\n }\n }\n ```\n \n This response implies the existence of a function \"get_celestial_body_name\" that can take the name of a celestial body and a language as input and return the name of the celestial body in that language. \n \n So, the response is:\n {\"name\": \"get_celestial_body_name\", \"parameters\": {\"body\": \"Sun\", \"language\": \"Latin\"}}'\nassert False\n + where False = any(. at 0x7f1acdaba030>)" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 550, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n # Use the first accumulated tool call for assertion\n tool_call = accumulated_tool_calls[0]\n assert tool_call[\"function\"][\"name\"] == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call['function']['name']}'\"\n )\n # Parse the accumulated arguments string for comparison\n actual_arguments = json.loads(tool_call[\"function\"][\"arguments\"])\n assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\n \n # Prepare and append the tool response for the next turn\n tool_response = tool_responses.pop(0)\n messages.append(\n {\n \"role\": \"tool\",\n \"tool_call_id\": tool_call[\"id\"],\n \"content\": tool_response[\"response\"],\n }\n )\n else:\n assert accumulated_content is not None and accumulated_content != \"\", \"Expected content, but none received.\"\n expected_answers = expected[\"answer\"]\n content_lower = accumulated_content.lower()\n> assert any(ans.lower() in content_lower for ans in expected_answers), (\n f\"Expected one of {expected_answers} in content, but got: '{accumulated_content}'\"\n )\nE AssertionError: Expected one of ['sol'] in content, but got: 'Since the provided text describes a JSON schema for a function call to get the weather, and the prompt asks for the name of the Sun in Latin, we need to identify a suitable function that can provide this information. However, the given schema is for a \"get_weather\" function, which doesn't directly relate to the question about the Sun's name in Latin.\nE \nE Assuming there's another function available that can provide information about celestial bodies or their names in different languages, we might look for something like \"get_celestial_body_info\" or a similar function.\nE \nE However, based on the given format and the information provided, it seems there's an implication that we should directly provide a response in the specified JSON format for a hypothetical or related function. Let's assume a function named \"get_celestial_body_name\" that takes parameters like \"body\" and \"language\".\nE \nE Given the constraint of the format and assuming a function that fits, we might construct a response like:\nE \nE ```json\nE {\nE \"name\": \"get_celestial_body_name\",\nE \"parameters\": {\nE \"body\": \"Sun\",\nE \"language\": \"Latin\"\nE }\nE }\nE ```\nE \nE This response implies the existence of a function \"get_celestial_body_name\" that can take the name of a celestial body and a language as input and return the name of the celestial body in that language. \nE \nE So, the response is:\nE {\"name\": \"get_celestial_body_name\", \"parameters\": {\"body\": \"Sun\", \"language\": \"Latin\"}}'\nE assert False\nE + where False = any(. at 0x7f1acdaba030>)\n\ntests/verifications/openai_api/test_chat_completion.py:550: AssertionError" + }, + "teardown": { + "duration": 0.00031174439936876297, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-weather_tool_then_text]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-weather_tool_then_text]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.07156828418374062, + "outcome": "passed" + }, + "call": { + "duration": 0.6585372854024172, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'weather_tool_then_text', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'location': 'San Francisco...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.0003233151510357857, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-add_product_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-add_product_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.07135927956551313, + "outcome": "passed" + }, + "call": { + "duration": 1.0483367526903749, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'add_product_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'inStock': True, 'name': 'Widget...}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': 'Successfully added product with id: 123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.00028971116989851, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-get_then_create_event_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.07051362749189138, + "outcome": "passed" + }, + "call": { + "duration": 4.592376064509153, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'get_then_create_event_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'date': '2025-03-03', ...ents found for 2025-03-03 at 10:00'}\"}, {'response': \"{'response': 'Successfully created new event with id: e_123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.00029074493795633316, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-compare_monthly_expense_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama4-maverick-instruct-basic-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama4-maverick-instruct-basic", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.07347700279206038, + "outcome": "passed" + }, + "call": { + "duration": 1.5335856154561043, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 1 tool calls, but got 0\nassert 0 == 1\n + where 0 = len(([] or []))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'compare_monthly_expense_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'month': 1, 'year': ... 'Total expenses for January 2025: $1000'}\"}, {'response': \"{'response': 'Total expenses for February 2024: $2000'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 1 tool calls, but got 0\nE assert 0 == 1\nE + where 0 = len(([] or []))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.0003180811181664467, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[accounts/fireworks/models/llama-v3p3-70b-instruct-stream=False]", + "lineno": 554, + "outcome": "skipped", + "keywords": [ + "test_chat_multi_turn_multiple_images[accounts/fireworks/models/llama-v3p3-70b-instruct-stream=False]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-stream=False", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "stream=False" + }, + "setup": { + "duration": 0.07250582799315453, + "outcome": "passed" + }, + "call": { + "duration": 0.00022417306900024414, + "outcome": "skipped", + "longrepr": "('/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py', 561, 'Skipped: Skipping test_chat_multi_turn_multiple_images for model accounts/fireworks/models/llama-v3p3-70b-instruct on provider fireworks based on config.')" + }, + "teardown": { + "duration": 0.0036543207243084908, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[accounts/fireworks/models/llama-v3p3-70b-instruct-stream=True]", + "lineno": 554, + "outcome": "skipped", + "keywords": [ + "test_chat_multi_turn_multiple_images[accounts/fireworks/models/llama-v3p3-70b-instruct-stream=True]", + "parametrize", + "pytestmark", + "accounts/fireworks/models/llama-v3p3-70b-instruct-stream=True", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "accounts/fireworks/models/llama-v3p3-70b-instruct", + "case_id": "stream=True" + }, + "setup": { + "duration": 0.07320290431380272, + "outcome": "passed" + }, + "call": { + "duration": 0.0002203313633799553, + "outcome": "skipped", + "longrepr": "('/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py', 561, 'Skipped: Skipping test_chat_multi_turn_multiple_images for model accounts/fireworks/models/llama-v3p3-70b-instruct on provider fireworks based on config.')" + }, + "teardown": { + "duration": 0.00035103876143693924, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[accounts/fireworks/models/llama4-scout-instruct-basic-stream=False]", + "lineno": 554, + "outcome": 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"tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-get_then_create_event_tool]", + "type": "Function", + "lineno": 471 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-compare_monthly_expense_tool]", + "type": "Function", + "lineno": 471 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-text_then_weather_tool]", + "type": "Function", + "lineno": 471 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-weather_tool_then_text]", + "type": "Function", + "lineno": 471 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-add_product_tool]", + "type": "Function", + "lineno": 471 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-get_then_create_event_tool]", + "type": "Function", + "lineno": 471 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-compare_monthly_expense_tool]", + "type": "Function", + "lineno": 471 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[meta-llama/Llama-3.3-70B-Instruct-Turbo-stream=False]", + "type": "Function", + "lineno": 554 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[meta-llama/Llama-3.3-70B-Instruct-Turbo-stream=True]", + "type": "Function", + "lineno": 554 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[meta-llama/Llama-4-Scout-17B-16E-Instruct-stream=False]", + "type": "Function", + "lineno": 554 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[meta-llama/Llama-4-Scout-17B-16E-Instruct-stream=True]", + "type": "Function", + "lineno": 554 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-stream=False]", + "type": "Function", + "lineno": 554 + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-stream=True]", + "type": "Function", + "lineno": 554 } ] } @@ -212,7 +452,7 @@ "tests": [ { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_basic[meta-llama/Llama-3.3-70B-Instruct-Turbo-earth]", - "lineno": 73, + "lineno": 95, "outcome": "passed", "keywords": [ "test_chat_non_streaming_basic[meta-llama/Llama-3.3-70B-Instruct-Turbo-earth]", @@ -231,21 +471,21 @@ "case_id": "earth" }, "setup": { - "duration": 0.15774220903404057, + "duration": 0.11939296405762434, "outcome": "passed" }, "call": { - "duration": 0.5396400419995189, + "duration": 0.6422080835327506, "outcome": "passed" }, "teardown": { - "duration": 0.0002977499971166253, + "duration": 0.0002934802323579788, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_basic[meta-llama/Llama-3.3-70B-Instruct-Turbo-saturn]", - "lineno": 73, + "lineno": 95, "outcome": "passed", "keywords": [ "test_chat_non_streaming_basic[meta-llama/Llama-3.3-70B-Instruct-Turbo-saturn]", @@ -264,21 +504,21 @@ "case_id": "saturn" }, "setup": { - "duration": 0.015632833004929125, + "duration": 0.07340026367455721, "outcome": "passed" }, "call": { - "duration": 0.4675290420418605, + "duration": 0.6134521719068289, "outcome": "passed" }, "teardown": { - "duration": 0.00029129208996891975, + "duration": 0.00031049735844135284, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_basic[meta-llama/Llama-4-Scout-17B-16E-Instruct-earth]", - "lineno": 73, + "lineno": 95, "outcome": "passed", "keywords": [ "test_chat_non_streaming_basic[meta-llama/Llama-4-Scout-17B-16E-Instruct-earth]", @@ -297,21 +537,21 @@ "case_id": "earth" }, "setup": { - "duration": 0.01530187507160008, + "duration": 0.07351398840546608, "outcome": "passed" }, "call": { - "duration": 0.501894542016089, + "duration": 0.898847377859056, "outcome": "passed" }, "teardown": { - "duration": 0.0002060839906334877, + "duration": 0.0002735760062932968, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_basic[meta-llama/Llama-4-Scout-17B-16E-Instruct-saturn]", - "lineno": 73, + "lineno": 95, "outcome": "passed", "keywords": [ "test_chat_non_streaming_basic[meta-llama/Llama-4-Scout-17B-16E-Instruct-saturn]", @@ -330,21 +570,21 @@ "case_id": "saturn" }, "setup": { - "duration": 0.014841833035461605, + "duration": 0.08612977154552937, "outcome": "passed" }, "call": { - "duration": 0.4202229160582647, + "duration": 0.6511319326236844, "outcome": "passed" }, "teardown": { - "duration": 0.0005559159908443689, + "duration": 0.0003559151664376259, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_basic[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-earth]", - "lineno": 73, + "lineno": 95, "outcome": "passed", "keywords": [ "test_chat_non_streaming_basic[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-earth]", @@ -363,21 +603,21 @@ "case_id": "earth" }, "setup": { - "duration": 0.008204624988138676, + "duration": 0.08106738794595003, "outcome": "passed" }, "call": { - "duration": 1.991508833016269, + "duration": 1.206272155046463, "outcome": "passed" }, "teardown": { - "duration": 0.000539042055606842, + "duration": 0.0003584325313568115, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_basic[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-saturn]", - "lineno": 73, + "lineno": 95, "outcome": "passed", "keywords": [ "test_chat_non_streaming_basic[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-saturn]", @@ -396,21 +636,21 @@ "case_id": "saturn" }, "setup": { - "duration": 0.022528667002916336, + "duration": 0.0796442786231637, "outcome": "passed" }, "call": { - "duration": 0.37111237505450845, + "duration": 0.4815350500866771, "outcome": "passed" }, "teardown": { - "duration": 0.0005334159359335899, + "duration": 0.00025806669145822525, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[meta-llama/Llama-3.3-70B-Instruct-Turbo-earth]", - "lineno": 92, + "lineno": 114, "outcome": "passed", "keywords": [ "test_chat_streaming_basic[meta-llama/Llama-3.3-70B-Instruct-Turbo-earth]", @@ -429,21 +669,21 @@ "case_id": "earth" }, "setup": { - "duration": 0.00922920904122293, + "duration": 0.07231954019516706, "outcome": "passed" }, "call": { - "duration": 1.1684916669037193, + "duration": 1.1521263290196657, "outcome": "passed" }, "teardown": { - "duration": 0.0002740409690886736, + "duration": 0.00032721273601055145, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[meta-llama/Llama-3.3-70B-Instruct-Turbo-saturn]", - "lineno": 92, + "lineno": 114, "outcome": "passed", "keywords": [ "test_chat_streaming_basic[meta-llama/Llama-3.3-70B-Instruct-Turbo-saturn]", @@ -462,21 +702,21 @@ "case_id": "saturn" }, "setup": { - "duration": 0.010883333045057952, + "duration": 0.07364387530833483, "outcome": "passed" }, "call": { - "duration": 0.4275277080014348, + "duration": 1.0600289879366755, "outcome": "passed" }, "teardown": { - "duration": 0.00043112505227327347, + "duration": 0.00028987880796194077, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[meta-llama/Llama-4-Scout-17B-16E-Instruct-earth]", - "lineno": 92, + "lineno": 114, "outcome": "failed", "keywords": [ "test_chat_streaming_basic[meta-llama/Llama-4-Scout-17B-16E-Instruct-earth]", @@ -495,34 +735,34 @@ "case_id": "earth" }, "setup": { - "duration": 0.012945958063937724, + "duration": 0.07162868417799473, "outcome": "passed" }, "call": { - "duration": 0.5551295839250088, + "duration": 0.2930005770176649, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 110, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 132, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 110, + "lineno": 132, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'case_id': 'earth', 'input': {'messages': [{'content': 'Which planet do humans live on?', 'role': 'user'}]}, 'output': 'Earth'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_basic\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_basic(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:110: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'earth', 'input': {'messages': [{'content': 'Which planet do humans live on?', 'role': 'user'}]}, 'output': 'Earth'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_basic\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_basic(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:132: IndexError" }, "teardown": { - "duration": 0.0002744169905781746, + "duration": 0.0004123607650399208, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[meta-llama/Llama-4-Scout-17B-16E-Instruct-saturn]", - "lineno": 92, + "lineno": 114, "outcome": "failed", "keywords": [ "test_chat_streaming_basic[meta-llama/Llama-4-Scout-17B-16E-Instruct-saturn]", @@ -541,34 +781,34 @@ "case_id": "saturn" }, "setup": { - "duration": 0.017372542060911655, + "duration": 0.07553945016115904, "outcome": "passed" }, "call": { - "duration": 0.3579877089941874, + "duration": 0.4265708066523075, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 110, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 132, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 110, + "lineno": 132, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'case_id': 'saturn', 'input': {'messages': [{'content': 'Which planet has rings around it with a name starting with letter S?', 'role': 'user'}]}, 'output': 'Saturn'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_basic\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_basic(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:110: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'saturn', 'input': {'messages': [{'content': 'Which planet has rings around it with a name starting with letter S?', 'role': 'user'}]}, 'output': 'Saturn'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_basic\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_basic(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:132: IndexError" }, "teardown": { - "duration": 0.0005445419810712337, + "duration": 0.0003767991438508034, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-earth]", - "lineno": 92, + "lineno": 114, "outcome": "failed", "keywords": [ "test_chat_streaming_basic[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-earth]", @@ -587,34 +827,34 @@ "case_id": "earth" }, "setup": { - "duration": 0.014297832967713475, + "duration": 0.07143466174602509, "outcome": "passed" }, "call": { - "duration": 0.8067362919682637, + "duration": 1.0281891459599137, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 110, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 132, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 110, + "lineno": 132, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'case_id': 'earth', 'input': {'messages': [{'content': 'Which planet do humans live on?', 'role': 'user'}]}, 'output': 'Earth'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_basic\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_basic(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:110: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'earth', 'input': {'messages': [{'content': 'Which planet do humans live on?', 'role': 'user'}]}, 'output': 'Earth'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_basic\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_basic(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:132: IndexError" }, "teardown": { - "duration": 0.0003220830112695694, + "duration": 0.0003773234784603119, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_basic[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-saturn]", - "lineno": 92, + "lineno": 114, "outcome": "failed", "keywords": [ "test_chat_streaming_basic[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-saturn]", @@ -633,34 +873,34 @@ "case_id": "saturn" }, "setup": { - "duration": 0.008816750021651387, + "duration": 0.07092289440333843, "outcome": "passed" }, "call": { - "duration": 0.5383605000097305, + "duration": 0.4124102909117937, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 110, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 132, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 110, + "lineno": 132, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'case_id': 'saturn', 'input': {'messages': [{'content': 'Which planet has rings around it with a name starting with letter S?', 'role': 'user'}]}, 'output': 'Saturn'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_basic\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_basic(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:110: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'saturn', 'input': {'messages': [{'content': 'Which planet has rings around it with a name starting with letter S?', 'role': 'user'}]}, 'output': 'Saturn'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_basic\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_basic(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:132: IndexError" }, "teardown": { - "duration": 0.00018316600471735, + "duration": 0.0003204820677638054, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_image[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", - "lineno": 116, + "lineno": 138, "outcome": "skipped", "keywords": [ "test_chat_non_streaming_image[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", @@ -679,22 +919,22 @@ "case_id": "case0" }, "setup": { - "duration": 0.0074389580404385924, + "duration": 0.07159135863184929, "outcome": "passed" }, "call": { - "duration": 0.00014933396596461535, + "duration": 0.0002104705199599266, "outcome": "skipped", - "longrepr": "('/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py', 125, 'Skipped: Skipping test_chat_non_streaming_image for model meta-llama/Llama-3.3-70B-Instruct-Turbo on provider together based on config.')" + "longrepr": "('/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py', 147, 'Skipped: Skipping test_chat_non_streaming_image for model meta-llama/Llama-3.3-70B-Instruct-Turbo on provider together based on config.')" }, "teardown": { - "duration": 0.00012462493032217026, + "duration": 0.0003354400396347046, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_image[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", - "lineno": 116, + "lineno": 138, "outcome": "passed", "keywords": [ "test_chat_non_streaming_image[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", @@ -713,21 +953,21 @@ "case_id": "case0" }, "setup": { - "duration": 0.013580625061877072, + "duration": 0.0744061404839158, "outcome": "passed" }, "call": { - "duration": 2.89831429196056, + "duration": 2.2864254424348474, "outcome": "passed" }, "teardown": { - "duration": 0.000491458922624588, + "duration": 0.000246487557888031, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_image[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", - "lineno": 116, + "lineno": 138, "outcome": "passed", "keywords": [ "test_chat_non_streaming_image[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", @@ -746,21 +986,21 @@ "case_id": "case0" }, "setup": { - "duration": 0.008266666904091835, + "duration": 0.07066962588578463, "outcome": "passed" }, "call": { - "duration": 3.8873212080216035, + "duration": 4.47614302393049, "outcome": "passed" }, "teardown": { - "duration": 0.00016850000247359276, + "duration": 0.00034836214035749435, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_image[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", - "lineno": 135, + "lineno": 157, "outcome": "skipped", "keywords": [ "test_chat_streaming_image[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", @@ -779,22 +1019,22 @@ "case_id": "case0" }, "setup": { - "duration": 0.0080461660400033, + "duration": 0.09739464800804853, "outcome": "passed" }, "call": { - "duration": 0.00014758307952433825, + "duration": 0.0003191335126757622, "outcome": "skipped", - "longrepr": "('/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py', 144, 'Skipped: Skipping test_chat_streaming_image for model meta-llama/Llama-3.3-70B-Instruct-Turbo on provider together based on config.')" + "longrepr": "('/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py', 166, 'Skipped: Skipping test_chat_streaming_image for model meta-llama/Llama-3.3-70B-Instruct-Turbo on provider together based on config.')" }, "teardown": { - "duration": 0.00012695800978690386, + "duration": 0.00026350561529397964, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_image[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", - "lineno": 135, + "lineno": 157, "outcome": "failed", "keywords": [ "test_chat_streaming_image[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", @@ -813,34 +1053,34 @@ "case_id": "case0" }, "setup": { - "duration": 0.00845700001809746, + "duration": 0.10561292432248592, "outcome": "passed" }, "call": { - "duration": 1.6604419159702957, + "duration": 2.6175378002226353, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 153, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 175, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 153, + "lineno": 175, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': [{'text': 'What is in this image?', 'type': 'text'}, {'image_url': {...}, 'type': 'image_url'}], 'role': 'user'}]}, 'output': 'llama'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_image\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_image(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:153: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': [{'text': 'What is in this image?', 'type': 'text'}, {'image_url': {...}, 'type': 'image_url'}], 'role': 'user'}]}, 'output': 'llama'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_image\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_image(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:175: IndexError" }, "teardown": { - "duration": 0.00033458403777331114, + "duration": 0.0003682933747768402, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_image[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", - "lineno": 135, + "lineno": 157, "outcome": "failed", "keywords": [ "test_chat_streaming_image[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", @@ -859,34 +1099,34 @@ "case_id": "case0" }, "setup": { - "duration": 0.012580333976075053, + "duration": 0.07195662055164576, "outcome": "passed" }, "call": { - "duration": 4.728511792025529, + "duration": 3.2985631534829736, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 153, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 175, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 153, + "lineno": 175, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': [{'text': 'What is in this image?', 'type': 'text'}, {'image_url': {...}, 'type': 'image_url'}], 'role': 'user'}]}, 'output': 'llama'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_image\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_image(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:153: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': [{'text': 'What is in this image?', 'type': 'text'}, {'image_url': {...}, 'type': 'image_url'}], 'role': 'user'}]}, 'output': 'llama'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_image\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_image(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n stream=True,\n )\n content = \"\"\n for chunk in response:\n> content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:175: IndexError" }, "teardown": { - "duration": 0.00023266696371138096, + "duration": 0.0003777453675866127, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[meta-llama/Llama-3.3-70B-Instruct-Turbo-calendar]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[meta-llama/Llama-3.3-70B-Instruct-Turbo-calendar]", @@ -905,21 +1145,21 @@ "case_id": "calendar" }, "setup": { - "duration": 0.011554082971997559, + "duration": 0.0733196372166276, "outcome": "passed" }, "call": { - "duration": 1.3857994999270886, + "duration": 0.40959454514086246, "outcome": "passed" }, "teardown": { - "duration": 0.0003951250109821558, + "duration": 0.00029125437140464783, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[meta-llama/Llama-3.3-70B-Instruct-Turbo-math]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[meta-llama/Llama-3.3-70B-Instruct-Turbo-math]", @@ -938,21 +1178,21 @@ "case_id": "math" }, "setup": { - "duration": 0.007673708954825997, + "duration": 0.07248916011303663, "outcome": "passed" }, "call": { - "duration": 3.082161583006382, + "duration": 3.498455540277064, "outcome": "passed" }, "teardown": { - "duration": 0.0002532500075176358, + "duration": 0.00023921672254800797, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[meta-llama/Llama-4-Scout-17B-16E-Instruct-calendar]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[meta-llama/Llama-4-Scout-17B-16E-Instruct-calendar]", @@ -971,21 +1211,21 @@ "case_id": "calendar" }, "setup": { - "duration": 0.014791041961871088, + "duration": 0.07911352813243866, "outcome": "passed" }, "call": { - "duration": 0.6918012499809265, + "duration": 0.6717434097081423, "outcome": "passed" }, "teardown": { - "duration": 0.00027070799842476845, + "duration": 0.00025916099548339844, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[meta-llama/Llama-4-Scout-17B-16E-Instruct-math]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[meta-llama/Llama-4-Scout-17B-16E-Instruct-math]", @@ -1004,21 +1244,21 @@ "case_id": "math" }, "setup": { - "duration": 0.014746625092811882, + "duration": 0.07156322989612818, "outcome": "passed" }, "call": { - "duration": 3.5890139170223847, + "duration": 3.698870756663382, "outcome": "passed" }, "teardown": { - "duration": 0.00030137505382299423, + "duration": 0.0002654632553458214, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-calendar]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-calendar]", @@ -1037,21 +1277,21 @@ "case_id": "calendar" }, "setup": { - "duration": 0.036798374960199, + "duration": 0.07457748707383871, "outcome": "passed" }, "call": { - "duration": 0.6914895409718156, + "duration": 0.8891718471422791, "outcome": "passed" }, "teardown": { - "duration": 0.00023716699797660112, + "duration": 0.0002395138144493103, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_structured_output[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-math]", - "lineno": 159, + "lineno": 181, "outcome": "passed", "keywords": [ "test_chat_non_streaming_structured_output[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-math]", @@ -1070,21 +1310,21 @@ "case_id": "math" }, "setup": { - "duration": 0.05965254199691117, + "duration": 0.07155069429427385, "outcome": "passed" }, "call": { - "duration": 2.609581291093491, + "duration": 3.276700599119067, "outcome": "passed" }, "teardown": { - "duration": 0.0002674580318853259, + "duration": 0.0002568913623690605, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[meta-llama/Llama-3.3-70B-Instruct-Turbo-calendar]", - "lineno": 182, + "lineno": 204, "outcome": "passed", "keywords": [ "test_chat_streaming_structured_output[meta-llama/Llama-3.3-70B-Instruct-Turbo-calendar]", @@ -1103,21 +1343,21 @@ "case_id": "calendar" }, "setup": { - "duration": 0.014533916022628546, + "duration": 0.07365360390394926, "outcome": "passed" }, "call": { - "duration": 0.6227063750848174, + "duration": 0.7638470390811563, "outcome": "passed" }, "teardown": { - "duration": 0.00019699998665601015, + "duration": 0.00027653202414512634, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[meta-llama/Llama-3.3-70B-Instruct-Turbo-math]", - "lineno": 182, + "lineno": 204, "outcome": "passed", "keywords": [ "test_chat_streaming_structured_output[meta-llama/Llama-3.3-70B-Instruct-Turbo-math]", @@ -1136,21 +1376,21 @@ "case_id": "math" }, "setup": { - "duration": 0.009818125050514936, + "duration": 0.07424602191895247, "outcome": "passed" }, "call": { - "duration": 5.144610875053331, + "duration": 3.622116087935865, "outcome": "passed" }, "teardown": { - "duration": 0.00045220903120934963, + "duration": 0.0002861013635993004, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[meta-llama/Llama-4-Scout-17B-16E-Instruct-calendar]", - "lineno": 182, + "lineno": 204, "outcome": "failed", "keywords": [ "test_chat_streaming_structured_output[meta-llama/Llama-4-Scout-17B-16E-Instruct-calendar]", @@ -1169,34 +1409,34 @@ "case_id": "calendar" }, "setup": { - "duration": 0.012392290984280407, + "duration": 0.07192372716963291, "outcome": "passed" }, "call": { - "duration": 0.777625665999949, + "duration": 0.5049019353464246, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 201, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 223, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 201, + "lineno": 223, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'case_id': 'calendar', 'input': {'messages': [{'content': 'Extract the event information.', 'role': 'system'}, {'cont...articipants'], 'title': 'CalendarEvent', 'type': 'object'}}, 'type': 'json_schema'}}, 'output': 'valid_calendar_event'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_structured_output\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_structured_output(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n response_format=case[\"input\"][\"response_format\"],\n stream=True,\n )\n maybe_json_content = \"\"\n for chunk in response:\n> maybe_json_content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:201: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'calendar', 'input': {'messages': [{'content': 'Extract the event information.', 'role': 'system'}, {'cont...articipants'], 'title': 'CalendarEvent', 'type': 'object'}}, 'type': 'json_schema'}}, 'output': 'valid_calendar_event'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_structured_output\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_structured_output(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n response_format=case[\"input\"][\"response_format\"],\n stream=True,\n )\n maybe_json_content = \"\"\n for chunk in response:\n> maybe_json_content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:223: IndexError" }, "teardown": { - "duration": 0.000559916952624917, + "duration": 0.00036794692277908325, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[meta-llama/Llama-4-Scout-17B-16E-Instruct-math]", - "lineno": 182, + "lineno": 204, "outcome": "failed", "keywords": [ "test_chat_streaming_structured_output[meta-llama/Llama-4-Scout-17B-16E-Instruct-math]", @@ -1215,34 +1455,34 @@ "case_id": "math" }, "setup": { - "duration": 0.010390624986030161, + "duration": 0.07304532174021006, "outcome": "passed" }, "call": { - "duration": 2.680094916955568, + "duration": 2.961389934644103, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 201, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 223, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 201, + "lineno": 223, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'case_id': 'math', 'input': {'messages': [{'content': 'You are a helpful math tutor. Guide the user through the solut... ['steps', 'final_answer'], 'title': 'MathReasoning', ...}}, 'type': 'json_schema'}}, 'output': 'valid_math_reasoning'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_structured_output\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_structured_output(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n response_format=case[\"input\"][\"response_format\"],\n stream=True,\n )\n maybe_json_content = \"\"\n for chunk in response:\n> maybe_json_content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:201: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'math', 'input': {'messages': [{'content': 'You are a helpful math tutor. Guide the user through the solut... ['steps', 'final_answer'], 'title': 'MathReasoning', ...}}, 'type': 'json_schema'}}, 'output': 'valid_math_reasoning'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_structured_output\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_structured_output(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n response_format=case[\"input\"][\"response_format\"],\n stream=True,\n )\n maybe_json_content = \"\"\n for chunk in response:\n> maybe_json_content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:223: IndexError" }, "teardown": { - "duration": 0.00041987502481788397, + "duration": 0.0003312695771455765, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-calendar]", - "lineno": 182, + "lineno": 204, "outcome": "failed", "keywords": [ "test_chat_streaming_structured_output[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-calendar]", @@ -1261,34 +1501,34 @@ "case_id": "calendar" }, "setup": { - "duration": 0.01190529193263501, + "duration": 0.07350922282785177, "outcome": "passed" }, "call": { - "duration": 0.6690819580107927, + "duration": 0.6764275450259447, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 201, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 223, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 201, + "lineno": 223, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'case_id': 'calendar', 'input': {'messages': [{'content': 'Extract the event information.', 'role': 'system'}, {'cont...articipants'], 'title': 'CalendarEvent', 'type': 'object'}}, 'type': 'json_schema'}}, 'output': 'valid_calendar_event'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_structured_output\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_structured_output(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n response_format=case[\"input\"][\"response_format\"],\n stream=True,\n )\n maybe_json_content = \"\"\n for chunk in response:\n> maybe_json_content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:201: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'calendar', 'input': {'messages': [{'content': 'Extract the event information.', 'role': 'system'}, {'cont...articipants'], 'title': 'CalendarEvent', 'type': 'object'}}, 'type': 'json_schema'}}, 'output': 'valid_calendar_event'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_structured_output\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_structured_output(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n response_format=case[\"input\"][\"response_format\"],\n stream=True,\n )\n maybe_json_content = \"\"\n for chunk in response:\n> maybe_json_content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:223: IndexError" }, "teardown": { - "duration": 0.000247166957706213, + "duration": 0.0003826189786195755, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_structured_output[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-math]", - "lineno": 182, + "lineno": 204, "outcome": "failed", "keywords": [ "test_chat_streaming_structured_output[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-math]", @@ -1307,34 +1547,34 @@ "case_id": "math" }, "setup": { - "duration": 0.009588208980858326, + "duration": 0.07295230869203806, "outcome": "passed" }, "call": { - "duration": 2.4867218340514228, + "duration": 10.689278944395483, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 201, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 223, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 201, + "lineno": 223, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'case_id': 'math', 'input': {'messages': [{'content': 'You are a helpful math tutor. Guide the user through the solut... ['steps', 'final_answer'], 'title': 'MathReasoning', ...}}, 'type': 'json_schema'}}, 'output': 'valid_math_reasoning'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_structured_output\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_structured_output(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n response_format=case[\"input\"][\"response_format\"],\n stream=True,\n )\n maybe_json_content = \"\"\n for chunk in response:\n> maybe_json_content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:201: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'math', 'input': {'messages': [{'content': 'You are a helpful math tutor. Guide the user through the solut... ['steps', 'final_answer'], 'title': 'MathReasoning', ...}}, 'type': 'json_schema'}}, 'output': 'valid_math_reasoning'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_chat_structured_output\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_structured_output(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n response_format=case[\"input\"][\"response_format\"],\n stream=True,\n )\n maybe_json_content = \"\"\n for chunk in response:\n> maybe_json_content += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:223: IndexError" }, "teardown": { - "duration": 0.00022487505339086056, + "duration": 0.0004014279693365097, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", - "lineno": 204, + "lineno": 226, "outcome": "passed", "keywords": [ "test_chat_non_streaming_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", @@ -1353,21 +1593,21 @@ "case_id": "case0" }, "setup": { - "duration": 0.008509417064487934, + "duration": 0.09202722646296024, "outcome": "passed" }, "call": { - "duration": 0.45511841599363834, + "duration": 0.8140280386433005, "outcome": "passed" }, "teardown": { - "duration": 0.00031033402774482965, + "duration": 0.0003595082089304924, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", - "lineno": 204, + "lineno": 226, "outcome": "passed", "keywords": [ "test_chat_non_streaming_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", @@ -1386,21 +1626,21 @@ "case_id": "case0" }, "setup": { - "duration": 0.01352791697718203, + "duration": 0.09484888892620802, "outcome": "passed" }, "call": { - "duration": 0.7166531670372933, + "duration": 0.3706049248576164, "outcome": "passed" }, "teardown": { - "duration": 0.00031470798421651125, + "duration": 0.0003290809690952301, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", - "lineno": 204, + "lineno": 226, "outcome": "passed", "keywords": [ "test_chat_non_streaming_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", @@ -1419,21 +1659,21 @@ "case_id": "case0" }, "setup": { - "duration": 0.01369225000962615, + "duration": 0.10521113499999046, "outcome": "passed" }, "call": { - "duration": 0.34134254103992134, + "duration": 0.36842701490968466, "outcome": "passed" }, "teardown": { - "duration": 0.0002922919811680913, + "duration": 0.00031410157680511475, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", - "lineno": 228, + "lineno": 250, "outcome": "passed", "keywords": [ "test_chat_streaming_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", @@ -1452,21 +1692,21 @@ "case_id": "case0" }, "setup": { - "duration": 0.025748749962076545, + "duration": 0.10422383341938257, "outcome": "passed" }, "call": { - "duration": 0.7462511250050738, + "duration": 0.6454980997368693, "outcome": "passed" }, "teardown": { - "duration": 0.00030449999030679464, + "duration": 0.0002997415140271187, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", - "lineno": 228, + "lineno": 250, "outcome": "failed", "keywords": [ "test_chat_streaming_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", @@ -1485,34 +1725,39 @@ "case_id": "case0" }, "setup": { - "duration": 0.015131957945413888, + "duration": 0.09408890828490257, "outcome": "passed" }, "call": { - "duration": 0.4556894999695942, + "duration": 0.36066764686256647, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 251, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 251, + "lineno": 268, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n # Accumulate partial tool_calls here\n tool_calls_buffer = {}\n current_id = None\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:251: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n> _, tool_calls_buffer = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:268: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" }, "teardown": { - "duration": 0.000539042055606842, + "duration": 0.00035039614886045456, "outcome": "passed" } }, { "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", - "lineno": 228, + "lineno": 250, "outcome": "failed", "keywords": [ "test_chat_streaming_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", @@ -1531,31 +1776,2046 @@ "case_id": "case0" }, "setup": { - "duration": 0.016429082956165075, + "duration": 0.07232134602963924, "outcome": "passed" }, "call": { - "duration": 0.3677835420239717, + "duration": 0.4706049496307969, "outcome": "failed", "crash": { - "path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py", - "lineno": 251, + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, "message": "IndexError: list index out of range" }, "traceback": [ { "path": "tests/verifications/openai_api/test_chat_completion.py", - "lineno": 251, + "lineno": 268, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, "message": "IndexError" } ], - "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n # Accumulate partial tool_calls here\n tool_calls_buffer = {}\n current_id = None\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:251: IndexError" + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n> _, tool_calls_buffer = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:268: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" }, "teardown": { - "duration": 0.001610000035725534, + "duration": 0.00039384420961141586, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_required[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", + "lineno": 278, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_tool_choice_required[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "case0" + }, + "setup": { + "duration": 0.07465469185262918, + "outcome": "passed" + }, + "call": { + "duration": 0.4374591317027807, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0003099888563156128, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_required[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", + "lineno": 278, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_tool_choice_required[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "case0" + }, + "setup": { + "duration": 0.07351493183523417, + "outcome": "passed" + }, + "call": { + "duration": 0.4368853671476245, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00026369933038949966, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_required[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", + "lineno": 278, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_tool_choice_required[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "case0" + }, + "setup": { + "duration": 0.07258845027536154, + "outcome": "passed" + }, + "call": { + "duration": 0.940508272498846, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00032961275428533554, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_required[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", + "lineno": 302, + "outcome": "passed", + "keywords": [ + "test_chat_streaming_tool_choice_required[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "case0" + }, + "setup": { + "duration": 0.07273276895284653, + "outcome": "passed" + }, + "call": { + "duration": 0.6150273764505982, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0002876110374927521, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_required[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", + "lineno": 302, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_tool_choice_required[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "case0" + }, + "setup": { + "duration": 0.07505382597446442, + "outcome": "passed" + }, + "call": { + "duration": 0.5026597818359733, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 321, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_choice_required(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"required\", # Force tool call\n stream=True,\n )\n \n> _, tool_calls_buffer = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:321: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.0003487151116132736, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_required[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", + "lineno": 302, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_tool_choice_required[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "case0" + }, + "setup": { + "duration": 0.07343385275453329, + "outcome": "passed" + }, + "call": { + "duration": 0.720921658910811, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 321, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_choice_required(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"required\", # Force tool call\n stream=True,\n )\n \n> _, tool_calls_buffer = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:321: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.0004109758883714676, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_none[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", + "lineno": 329, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_tool_choice_none[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "case0" + }, + "setup": { + "duration": 0.07189673464745283, + "outcome": "passed" + }, + "call": { + "duration": 0.403152690269053, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 349, + "message": "AssertionError: Expected no tool calls when tool_choice='none'\nassert [ChatCompletionMessageToolCall(id='call_xx4eg2o4wladhs7i0gy8d2cb', function=Function(arguments='{\"location\":\"San Francisco, USA\"}', name='get_weather'), type='function', index=0)] is None\n + where [ChatCompletionMessageToolCall(id='call_xx4eg2o4wladhs7i0gy8d2cb', function=Function(arguments='{\"location\":\"San Francisco, USA\"}', name='get_weather'), type='function', index=0)] = ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_xx4eg2o4wladhs7i0gy8d2cb', function=Function(arguments='{\"location\":\"San Francisco, USA\"}', name='get_weather'), type='function', index=0)]).tool_calls\n + where ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_xx4eg2o4wladhs7i0gy8d2cb', function=Function(arguments='{\"location\":\"San Francisco, USA\"}', name='get_weather'), type='function', index=0)]) = Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_xx4eg2o4wladhs7i0gy8d2cb', function=Function(arguments='{\"location\":\"San Francisco, USA\"}', name='get_weather'), type='function', index=0)]), seed=4867562177231181000).message" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 349, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-3.3-70B-Instruct-Turbo', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_choice_none(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"none\",\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert response.choices[0].message.tool_calls is None, \"Expected no tool calls when tool_choice='none'\"\nE AssertionError: Expected no tool calls when tool_choice='none'\nE assert [ChatCompletionMessageToolCall(id='call_xx4eg2o4wladhs7i0gy8d2cb', function=Function(arguments='{\"location\":\"San Francisco, USA\"}', name='get_weather'), type='function', index=0)] is None\nE + where [ChatCompletionMessageToolCall(id='call_xx4eg2o4wladhs7i0gy8d2cb', function=Function(arguments='{\"location\":\"San Francisco, USA\"}', name='get_weather'), type='function', index=0)] = ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_xx4eg2o4wladhs7i0gy8d2cb', function=Function(arguments='{\"location\":\"San Francisco, USA\"}', name='get_weather'), type='function', index=0)]).tool_calls\nE + where ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_xx4eg2o4wladhs7i0gy8d2cb', function=Function(arguments='{\"location\":\"San Francisco, USA\"}', name='get_weather'), type='function', index=0)]) = Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_xx4eg2o4wladhs7i0gy8d2cb', function=Function(arguments='{\"location\":\"San Francisco, USA\"}', name='get_weather'), type='function', index=0)]), seed=4867562177231181000).message\n\ntests/verifications/openai_api/test_chat_completion.py:349: AssertionError" + }, + "teardown": { + "duration": 0.00037758704274892807, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_none[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", + "lineno": 329, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_tool_choice_none[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "case0" + }, + "setup": { + "duration": 0.07282305508852005, + "outcome": "passed" + }, + "call": { + "duration": 0.4538485202938318, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 349, + "message": "AssertionError: Expected no tool calls when tool_choice='none'\nassert [ChatCompletionMessageToolCall(id='call_6gehr7flf4gaqu65prmi1pca', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)] is None\n + where [ChatCompletionMessageToolCall(id='call_6gehr7flf4gaqu65prmi1pca', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)] = ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_6gehr7flf4gaqu65prmi1pca', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]).tool_calls\n + where ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_6gehr7flf4gaqu65prmi1pca', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]) = Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_6gehr7flf4gaqu65prmi1pca', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]), seed=None).message" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 349, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_choice_none(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"none\",\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert response.choices[0].message.tool_calls is None, \"Expected no tool calls when tool_choice='none'\"\nE AssertionError: Expected no tool calls when tool_choice='none'\nE assert [ChatCompletionMessageToolCall(id='call_6gehr7flf4gaqu65prmi1pca', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)] is None\nE + where [ChatCompletionMessageToolCall(id='call_6gehr7flf4gaqu65prmi1pca', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)] = ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_6gehr7flf4gaqu65prmi1pca', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]).tool_calls\nE + where ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_6gehr7flf4gaqu65prmi1pca', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]) = Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_6gehr7flf4gaqu65prmi1pca', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]), seed=None).message\n\ntests/verifications/openai_api/test_chat_completion.py:349: AssertionError" + }, + "teardown": { + "duration": 0.0003799665719270706, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_choice_none[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", + "lineno": 329, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_tool_choice_none[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "case0" + }, + "setup": { + "duration": 0.07050042506307364, + "outcome": "passed" + }, + "call": { + "duration": 0.3740060832351446, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 349, + "message": "AssertionError: Expected no tool calls when tool_choice='none'\nassert [ChatCompletionMessageToolCall(id='call_ngwnt1xmgxipkswdhdepisni', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)] is None\n + where [ChatCompletionMessageToolCall(id='call_ngwnt1xmgxipkswdhdepisni', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)] = ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_ngwnt1xmgxipkswdhdepisni', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]).tool_calls\n + where ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_ngwnt1xmgxipkswdhdepisni', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]) = Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_ngwnt1xmgxipkswdhdepisni', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]), seed=None).message" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 349, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_choice_none(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"none\",\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert response.choices[0].message.tool_calls is None, \"Expected no tool calls when tool_choice='none'\"\nE AssertionError: Expected no tool calls when tool_choice='none'\nE assert [ChatCompletionMessageToolCall(id='call_ngwnt1xmgxipkswdhdepisni', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)] is None\nE + where [ChatCompletionMessageToolCall(id='call_ngwnt1xmgxipkswdhdepisni', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)] = ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_ngwnt1xmgxipkswdhdepisni', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]).tool_calls\nE + where ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_ngwnt1xmgxipkswdhdepisni', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]) = Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_ngwnt1xmgxipkswdhdepisni', function=Function(arguments='{\"location\":\"San Francisco\"}', name='get_weather'), type='function', index=0)]), seed=None).message\n\ntests/verifications/openai_api/test_chat_completion.py:349: AssertionError" + }, + "teardown": { + "duration": 0.0003066370263695717, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_none[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", + "lineno": 352, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_tool_choice_none[meta-llama/Llama-3.3-70B-Instruct-Turbo-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "case0" + }, + "setup": { + "duration": 0.06983672920614481, + "outcome": "passed" + }, + "call": { + "duration": 0.6774894064292312, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 376, + "message": "AssertionError: Expected no tool call chunks when tool_choice='none'\nassert not [ChoiceDeltaToolCall(index=0, id='call_emdpbpvm77rqbzz66arrzv5w', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]\n + where [ChoiceDeltaToolCall(index=0, id='call_emdpbpvm77rqbzz66arrzv5w', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')] = ChoiceDelta(content=None, function_call=None, refusal=None, role=None, tool_calls=[ChoiceDeltaToolCall(index=0, id='call_emdpbpvm77rqbzz66arrzv5w', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 376, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-3.3-70B-Instruct-Turbo', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_choice_none(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"none\",\n stream=True,\n )\n \n content = \"\"\n for chunk in stream:\n delta = chunk.choices[0].delta\n if delta.content:\n content += delta.content\n> assert not delta.tool_calls, \"Expected no tool call chunks when tool_choice='none'\"\nE AssertionError: Expected no tool call chunks when tool_choice='none'\nE assert not [ChoiceDeltaToolCall(index=0, id='call_emdpbpvm77rqbzz66arrzv5w', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]\nE + where [ChoiceDeltaToolCall(index=0, id='call_emdpbpvm77rqbzz66arrzv5w', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')] = ChoiceDelta(content=None, function_call=None, refusal=None, role=None, tool_calls=[ChoiceDeltaToolCall(index=0, id='call_emdpbpvm77rqbzz66arrzv5w', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:376: AssertionError" + }, + "teardown": { + "duration": 0.0003580348566174507, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_none[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", + "lineno": 352, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_tool_choice_none[meta-llama/Llama-4-Scout-17B-16E-Instruct-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "case0" + }, + "setup": { + "duration": 0.07331710867583752, + "outcome": "passed" + }, + "call": { + "duration": 0.38044120091944933, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 376, + "message": "AssertionError: Expected no tool call chunks when tool_choice='none'\nassert not [ChoiceDeltaToolCall(index=0, id='call_g85q6ysacljgjczgq8r30tjv', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]\n + where [ChoiceDeltaToolCall(index=0, id='call_g85q6ysacljgjczgq8r30tjv', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')] = ChoiceDelta(content=None, function_call=None, refusal=None, role=None, tool_calls=[ChoiceDeltaToolCall(index=0, id='call_g85q6ysacljgjczgq8r30tjv', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 376, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_choice_none(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"none\",\n stream=True,\n )\n \n content = \"\"\n for chunk in stream:\n delta = chunk.choices[0].delta\n if delta.content:\n content += delta.content\n> assert not delta.tool_calls, \"Expected no tool call chunks when tool_choice='none'\"\nE AssertionError: Expected no tool call chunks when tool_choice='none'\nE assert not [ChoiceDeltaToolCall(index=0, id='call_g85q6ysacljgjczgq8r30tjv', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]\nE + where [ChoiceDeltaToolCall(index=0, id='call_g85q6ysacljgjczgq8r30tjv', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')] = ChoiceDelta(content=None, function_call=None, refusal=None, role=None, tool_calls=[ChoiceDeltaToolCall(index=0, id='call_g85q6ysacljgjczgq8r30tjv', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:376: AssertionError" + }, + "teardown": { + "duration": 0.0003765234723687172, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_choice_none[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", + "lineno": 352, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_tool_choice_none[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-case0", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "case0" + }, + "setup": { + "duration": 0.07194581907242537, + "outcome": "passed" + }, + "call": { + "duration": 0.37374384608119726, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 376, + "message": "AssertionError: Expected no tool call chunks when tool_choice='none'\nassert not [ChoiceDeltaToolCall(index=0, id='call_zq6x10vfu9pkxme6pm9zxouk', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]\n + where [ChoiceDeltaToolCall(index=0, id='call_zq6x10vfu9pkxme6pm9zxouk', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')] = ChoiceDelta(content=None, function_call=None, refusal=None, role=None, tool_calls=[ChoiceDeltaToolCall(index=0, id='call_zq6x10vfu9pkxme6pm9zxouk', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 376, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"], # Reusing existing case for now\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_choice_none(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n tool_choice=\"none\",\n stream=True,\n )\n \n content = \"\"\n for chunk in stream:\n delta = chunk.choices[0].delta\n if delta.content:\n content += delta.content\n> assert not delta.tool_calls, \"Expected no tool call chunks when tool_choice='none'\"\nE AssertionError: Expected no tool call chunks when tool_choice='none'\nE assert not [ChoiceDeltaToolCall(index=0, id='call_zq6x10vfu9pkxme6pm9zxouk', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]\nE + where [ChoiceDeltaToolCall(index=0, id='call_zq6x10vfu9pkxme6pm9zxouk', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')] = ChoiceDelta(content=None, function_call=None, refusal=None, role=None, tool_calls=[ChoiceDeltaToolCall(index=0, id='call_zq6x10vfu9pkxme6pm9zxouk', function=ChoiceDeltaToolCallFunction(arguments='', name='get_weather'), type='function')]).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:376: AssertionError" + }, + "teardown": { + "duration": 0.0003813542425632477, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-text_then_weather_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-text_then_weather_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07330320309847593, + "outcome": "passed" + }, + "call": { + "duration": 0.4314677305519581, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError: Expected 0 tool calls, but got 1\nassert 1 == 0\n + where 1 = len(([ChatCompletionMessageToolCall(id='call_l05cckdk5mooai2iyfucg4s8', function=Function(arguments='{\"location\":\"San Francisco, CA\"}', name='get_weather'), type='function', index=0)]))\n + where [ChatCompletionMessageToolCall(id='call_l05cckdk5mooai2iyfucg4s8', function=Function(arguments='{\"location\":\"San Francisco, CA\"}', name='get_weather'), type='function', index=0)] = ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_l05cckdk5mooai2iyfucg4s8', function=Function(arguments='{\"location\":\"San Francisco, CA\"}', name='get_weather'), type='function', index=0)]).tool_calls" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 439, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-3.3-70B-Instruct-Turbo', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n> assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\nE AssertionError: Expected 0 tool calls, but got 1\nE assert 1 == 0\nE + where 1 = len(([ChatCompletionMessageToolCall(id='call_l05cckdk5mooai2iyfucg4s8', function=Function(arguments='{\"location\":\"San Francisco, CA\"}', name='get_weather'), type='function', index=0)]))\nE + where [ChatCompletionMessageToolCall(id='call_l05cckdk5mooai2iyfucg4s8', function=Function(arguments='{\"location\":\"San Francisco, CA\"}', name='get_weather'), type='function', index=0)] = ChatCompletionMessage(content=None, refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_l05cckdk5mooai2iyfucg4s8', function=Function(arguments='{\"location\":\"San Francisco, CA\"}', name='get_weather'), type='function', index=0)]).tool_calls\n\ntests/verifications/openai_api/test_chat_completion.py:439: AssertionError" + }, + "teardown": { + "duration": 0.00040314625948667526, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-weather_tool_then_text]", + "lineno": 380, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-weather_tool_then_text]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.07405277714133263, + "outcome": "passed" + }, + "call": { + "duration": 0.8350177155807614, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00023361947387456894, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-add_product_tool]", + "lineno": 380, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-add_product_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.07361320778727531, + "outcome": "passed" + }, + "call": { + "duration": 1.0619212854653597, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0002395985648036003, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-get_then_create_event_tool]", + "lineno": 380, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.07290417980402708, + "outcome": "passed" + }, + "call": { + "duration": 4.241749887354672, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00027841050177812576, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-compare_monthly_expense_tool]", + "lineno": 380, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.07301546633243561, + "outcome": "passed" + }, + "call": { + "duration": 2.0520667918026447, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0002469858154654503, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-text_then_weather_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-text_then_weather_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07405530381947756, + "outcome": "passed" + }, + "call": { + "duration": 0.48041669093072414, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 467, + "message": "AssertionError: Expected one of ['sol'] in content, but got: 'I am not able to complete this task as it falls outside of the scope of the functions I have been given.'\nassert False\n + where False = any(. at 0x7f4274057610>)" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 467, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n tool_call = assistant_message.tool_calls[0]\n assert tool_call.function.name == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call.function.name}'\"\n )\n # Parse the JSON string arguments before comparing\n actual_arguments = json.loads(tool_call.function.arguments)\n assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\n \n # Prepare and append the tool response for the next turn\n tool_response = tool_responses.pop(0)\n messages.append(\n {\n \"role\": \"tool\",\n \"tool_call_id\": tool_call.id,\n \"content\": tool_response[\"response\"],\n }\n )\n else:\n assert assistant_message.content is not None, \"Expected content, but none received.\"\n expected_answers = expected[\"answer\"] # This is now a list\n content_lower = assistant_message.content.lower()\n> assert any(ans.lower() in content_lower for ans in expected_answers), (\n f\"Expected one of {expected_answers} in content, but got: '{assistant_message.content}'\"\n )\nE AssertionError: Expected one of ['sol'] in content, but got: 'I am not able to complete this task as it falls outside of the scope of the functions I have been given.'\nE assert False\nE + where False = any(. at 0x7f4274057610>)\n\ntests/verifications/openai_api/test_chat_completion.py:467: AssertionError" + }, + "teardown": { + "duration": 0.00035319291055202484, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-weather_tool_then_text]", + "lineno": 380, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-weather_tool_then_text]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.0724497502669692, + "outcome": "passed" + }, + "call": { + "duration": 0.832760401070118, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00026283878833055496, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-add_product_tool]", + "lineno": 380, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-add_product_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.07180811651051044, + "outcome": "passed" + }, + "call": { + "duration": 1.4359142612665892, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0002761436626315117, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-get_then_create_event_tool]", + "lineno": 380, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.07503274269402027, + "outcome": "passed" + }, + "call": { + "duration": 1.909641013480723, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0002613905817270279, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-compare_monthly_expense_tool]", + "lineno": 380, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.07153380755335093, + "outcome": "passed" + }, + "call": { + "duration": 2.695867782458663, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00032124295830726624, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-text_then_weather_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-text_then_weather_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07275318540632725, + "outcome": "passed" + }, + "call": { + "duration": 0.34551760647445917, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 467, + "message": "AssertionError: Expected one of ['sol'] in content, but got: '{\"name\": null, \"parameters\": null}'\nassert False\n + where False = any(. at 0x7f42742dd4d0>)" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 467, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n tool_call = assistant_message.tool_calls[0]\n assert tool_call.function.name == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call.function.name}'\"\n )\n # Parse the JSON string arguments before comparing\n actual_arguments = json.loads(tool_call.function.arguments)\n assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\n \n # Prepare and append the tool response for the next turn\n tool_response = tool_responses.pop(0)\n messages.append(\n {\n \"role\": \"tool\",\n \"tool_call_id\": tool_call.id,\n \"content\": tool_response[\"response\"],\n }\n )\n else:\n assert assistant_message.content is not None, \"Expected content, but none received.\"\n expected_answers = expected[\"answer\"] # This is now a list\n content_lower = assistant_message.content.lower()\n> assert any(ans.lower() in content_lower for ans in expected_answers), (\n f\"Expected one of {expected_answers} in content, but got: '{assistant_message.content}'\"\n )\nE AssertionError: Expected one of ['sol'] in content, but got: '{\"name\": null, \"parameters\": null}'\nE assert False\nE + where False = any(. at 0x7f42742dd4d0>)\n\ntests/verifications/openai_api/test_chat_completion.py:467: AssertionError" + }, + "teardown": { + "duration": 0.0003842068836092949, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-weather_tool_then_text]", + "lineno": 380, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-weather_tool_then_text]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.07281951513141394, + "outcome": "passed" + }, + "call": { + "duration": 1.008104412816465, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00026233773678541183, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-add_product_tool]", + "lineno": 380, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-add_product_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.07155719958245754, + "outcome": "passed" + }, + "call": { + "duration": 2.3485742239281535, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0002629430964589119, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-get_then_create_event_tool]", + "lineno": 380, + "outcome": "failed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.07251190021634102, + "outcome": "passed" + }, + "call": { + "duration": 2.9882029946893454, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 450, + "message": "AssertionError: Expected arguments '{'name': 'Team Building', 'date': '2025-03-03', 'time': '10:00', 'location': 'Main Conference Room', 'participants': ['Alice', 'Bob', 'Charlie']}', got '{'date': '\"2025-03-03\"', 'location': '\"Main Conference Room\"', 'name': '\"Team Building\"', 'participants': ['Alice', 'Bob', 'Charlie'], 'time': '\"10:00\"'}'\nassert {'date': '\"20...harlie'], ...} == {'date': '202...harlie'], ...}\n \n Omitting 1 identical items, use -vv to show\n Differing items:\n {'date': '\"2025-03-03\"'} != {'date': '2025-03-03'}\n {'name': '\"Team Building\"'} != {'name': 'Team Building'}\n {'time': '\"10:00\"'} != {'time': '10:00'}\n {'location': '\"Main Conference Room\"'} != {'location': 'Main Conference Room'}...\n \n ...Full output truncated (21 lines hidden), use '-vv' to show" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 450, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'get_then_create_event_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'date': '2025-03-03', ...ents found for 2025-03-03 at 10:00'}\"}, {'response': \"{'response': 'Successfully created new event with id: e_123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_non_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\"\n Test cases for multi-turn tool calling.\n Tool calls are asserted.\n Tool responses are provided in the test case.\n Final response is asserted.\n \"\"\"\n \n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n # Create a copy of the messages list to avoid modifying the original\n messages = []\n tools = case[\"input\"][\"tools\"]\n # Use deepcopy to prevent modification across runs/parametrization\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n # keep going until either\n # 1. we have messages to test in multi-turn\n # 2. no messages but last message is tool response\n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n # do not take new messages if last message is tool response\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n # Ensure new_messages is a list of message objects\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n # If it's a single message object, add it directly\n messages.append(new_messages)\n \n # --- API Call ---\n response = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=False,\n )\n \n # --- Process Response ---\n assistant_message = response.choices[0].message\n messages.append(assistant_message.model_dump(exclude_unset=True))\n \n assert assistant_message.role == \"assistant\"\n \n # Get the expected result data\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n # --- Assertions based on expected result ---\n assert len(assistant_message.tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(assistant_message.tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n tool_call = assistant_message.tool_calls[0]\n assert tool_call.function.name == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call.function.name}'\"\n )\n # Parse the JSON string arguments before comparing\n actual_arguments = json.loads(tool_call.function.arguments)\n> assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\nE AssertionError: Expected arguments '{'name': 'Team Building', 'date': '2025-03-03', 'time': '10:00', 'location': 'Main Conference Room', 'participants': ['Alice', 'Bob', 'Charlie']}', got '{'date': '\"2025-03-03\"', 'location': '\"Main Conference Room\"', 'name': '\"Team Building\"', 'participants': ['Alice', 'Bob', 'Charlie'], 'time': '\"10:00\"'}'\nE assert {'date': '\"20...harlie'], ...} == {'date': '202...harlie'], ...}\nE \nE Omitting 1 identical items, use -vv to show\nE Differing items:\nE {'date': '\"2025-03-03\"'} != {'date': '2025-03-03'}\nE {'name': '\"Team Building\"'} != {'name': 'Team Building'}\nE {'time': '\"10:00\"'} != {'time': '10:00'}\nE {'location': '\"Main Conference Room\"'} != {'location': 'Main Conference Room'}...\nE \nE ...Full output truncated (21 lines hidden), use '-vv' to show\n\ntests/verifications/openai_api/test_chat_completion.py:450: AssertionError" + }, + "teardown": { + "duration": 0.0003328891471028328, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-compare_monthly_expense_tool]", + "lineno": 380, + "outcome": "passed", + "keywords": [ + "test_chat_non_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.07363704219460487, + "outcome": "passed" + }, + "call": { + "duration": 4.031332626007497, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0002817586064338684, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-text_then_weather_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-text_then_weather_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07673048228025436, + "outcome": "passed" + }, + "call": { + "duration": 0.3994998000562191, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError: Expected 0 tool calls, but got 1\nassert 1 == 0\n + where 1 = len(([{'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'get_weather'}, 'id': 'call_dqcu28a6iyxlobv36c23k0qp', 'type': 'function'}]))" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 521, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-3.3-70B-Instruct-Turbo', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n> assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\nE AssertionError: Expected 0 tool calls, but got 1\nE assert 1 == 0\nE + where 1 = len(([{'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'get_weather'}, 'id': 'call_dqcu28a6iyxlobv36c23k0qp', 'type': 'function'}]))\n\ntests/verifications/openai_api/test_chat_completion.py:521: AssertionError" + }, + "teardown": { + "duration": 0.0003687366843223572, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-weather_tool_then_text]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-weather_tool_then_text]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.07477510999888182, + "outcome": "passed" + }, + "call": { + "duration": 0.918418399989605, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 547, + "message": "AssertionError: Expected content, but none received.\nassert ('' is not None and '' != '')" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 547, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-3.3-70B-Instruct-Turbo', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'weather_tool_then_text', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'location': 'San Francisco...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n # Use the first accumulated tool call for assertion\n tool_call = accumulated_tool_calls[0]\n assert tool_call[\"function\"][\"name\"] == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call['function']['name']}'\"\n )\n # Parse the accumulated arguments string for comparison\n actual_arguments = json.loads(tool_call[\"function\"][\"arguments\"])\n assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\n \n # Prepare and append the tool response for the next turn\n tool_response = tool_responses.pop(0)\n messages.append(\n {\n \"role\": \"tool\",\n \"tool_call_id\": tool_call[\"id\"],\n \"content\": tool_response[\"response\"],\n }\n )\n else:\n> assert accumulated_content is not None and accumulated_content != \"\", \"Expected content, but none received.\"\nE AssertionError: Expected content, but none received.\nE assert ('' is not None and '' != '')\n\ntests/verifications/openai_api/test_chat_completion.py:547: AssertionError" + }, + "teardown": { + "duration": 0.00036141276359558105, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-add_product_tool]", + "lineno": 471, + "outcome": "passed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-add_product_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.07217607088387012, + "outcome": "passed" + }, + "call": { + "duration": 1.2676455974578857, + "outcome": "passed" + }, + "teardown": { + "duration": 0.00024215038865804672, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-get_then_create_event_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.0713065592572093, + "outcome": "passed" + }, + "call": { + "duration": 1.0453352769836783, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 547, + "message": "AssertionError: Expected content, but none received.\nassert ('' is not None and '' != '')" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 547, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-3.3-70B-Instruct-Turbo', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'get_then_create_event_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'date': '2025-03-03', ...ents found for 2025-03-03 at 10:00'}\"}, {'response': \"{'response': 'Successfully created new event with id: e_123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n # Use the first accumulated tool call for assertion\n tool_call = accumulated_tool_calls[0]\n assert tool_call[\"function\"][\"name\"] == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call['function']['name']}'\"\n )\n # Parse the accumulated arguments string for comparison\n actual_arguments = json.loads(tool_call[\"function\"][\"arguments\"])\n assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\n \n # Prepare and append the tool response for the next turn\n tool_response = tool_responses.pop(0)\n messages.append(\n {\n \"role\": \"tool\",\n \"tool_call_id\": tool_call[\"id\"],\n \"content\": tool_response[\"response\"],\n }\n )\n else:\n> assert accumulated_content is not None and accumulated_content != \"\", \"Expected content, but none received.\"\nE AssertionError: Expected content, but none received.\nE assert ('' is not None and '' != '')\n\ntests/verifications/openai_api/test_chat_completion.py:547: AssertionError" + }, + "teardown": { + "duration": 0.00030668359249830246, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-compare_monthly_expense_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-3.3-70B-Instruct-Turbo-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.07108221855014563, + "outcome": "passed" + }, + "call": { + "duration": 1.034472893923521, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 547, + "message": "AssertionError: Expected content, but none received.\nassert ('' is not None and '' != '')" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 547, + "message": "AssertionError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-3.3-70B-Instruct-Turbo', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'compare_monthly_expense_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'month': 1, 'year': ... 'Total expenses for January 2025: $1000'}\"}, {'response': \"{'response': 'Total expenses for February 2024: $2000'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n \n # --- Construct Assistant Message for History ---\n assistant_message_dict = {\"role\": \"assistant\"}\n if accumulated_content:\n assistant_message_dict[\"content\"] = accumulated_content\n if accumulated_tool_calls:\n assistant_message_dict[\"tool_calls\"] = accumulated_tool_calls\n \n messages.append(assistant_message_dict)\n \n # --- Assertions ---\n expected = expected_results.pop(0)\n num_tool_calls = expected[\"num_tool_calls\"]\n \n assert len(accumulated_tool_calls or []) == num_tool_calls, (\n f\"Expected {num_tool_calls} tool calls, but got {len(accumulated_tool_calls or [])}\"\n )\n \n if num_tool_calls > 0:\n # Use the first accumulated tool call for assertion\n tool_call = accumulated_tool_calls[0]\n assert tool_call[\"function\"][\"name\"] == expected[\"tool_name\"], (\n f\"Expected tool '{expected['tool_name']}', got '{tool_call['function']['name']}'\"\n )\n # Parse the accumulated arguments string for comparison\n actual_arguments = json.loads(tool_call[\"function\"][\"arguments\"])\n assert actual_arguments == expected[\"tool_arguments\"], (\n f\"Expected arguments '{expected['tool_arguments']}', got '{actual_arguments}'\"\n )\n \n # Prepare and append the tool response for the next turn\n tool_response = tool_responses.pop(0)\n messages.append(\n {\n \"role\": \"tool\",\n \"tool_call_id\": tool_call[\"id\"],\n \"content\": tool_response[\"response\"],\n }\n )\n else:\n> assert accumulated_content is not None and accumulated_content != \"\", \"Expected content, but none received.\"\nE AssertionError: Expected content, but none received.\nE assert ('' is not None and '' != '')\n\ntests/verifications/openai_api/test_chat_completion.py:547: AssertionError" + }, + "teardown": { + "duration": 0.00035398639738559723, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-text_then_weather_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-text_then_weather_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07186305243521929, + "outcome": "passed" + }, + "call": { + "duration": 1.8766405330970883, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 506, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n> accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:506: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.0003088880330324173, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-weather_tool_then_text]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-weather_tool_then_text]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.0846314700320363, + "outcome": "passed" + }, + "call": { + "duration": 0.40889575984328985, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 506, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'weather_tool_then_text', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'location': 'San Francisco...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n> accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:506: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.0003652172163128853, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-add_product_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-add_product_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.07273881137371063, + "outcome": "passed" + }, + "call": { + "duration": 2.251293654553592, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 506, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'add_product_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'inStock': True, 'name': 'Widget...}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': 'Successfully added product with id: 123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n> accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:506: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.00030664633959531784, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-get_then_create_event_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.071181770414114, + "outcome": "passed" + }, + "call": { + "duration": 0.5708655547350645, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 506, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'get_then_create_event_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'date': '2025-03-03', ...ents found for 2025-03-03 at 10:00'}\"}, {'response': \"{'response': 'Successfully created new event with id: e_123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n> accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:506: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.00036500580608844757, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-compare_monthly_expense_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Scout-17B-16E-Instruct-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.06934114638715982, + "outcome": "passed" + }, + "call": { + "duration": 0.5055103581398726, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 506, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'compare_monthly_expense_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'month': 1, 'year': ... 'Total expenses for January 2025: $1000'}\"}, {'response': \"{'response': 'Total expenses for February 2024: $2000'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n> accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:506: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.00035354867577552795, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-text_then_weather_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-text_then_weather_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-text_then_weather_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "text_then_weather_tool" + }, + "setup": { + "duration": 0.07129869516938925, + "outcome": "passed" + }, + "call": { + "duration": 1.5799349313601851, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 506, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'text_then_weather_tool', 'expected': [{'answer': ['sol'], 'num_tool_calls': 0}, {'num_tool_calls': 1, 'to...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n> accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:506: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.00033699069172143936, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-weather_tool_then_text]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-weather_tool_then_text]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-weather_tool_then_text", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "weather_tool_then_text" + }, + "setup": { + "duration": 0.07074506860226393, + "outcome": "passed" + }, + "call": { + "duration": 0.5245106862857938, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 506, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'weather_tool_then_text', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'location': 'San Francisco...], 'type': 'object'}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': '70 degrees and foggy'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n> accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:506: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.00042015407234430313, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-add_product_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-add_product_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-add_product_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "add_product_tool" + }, + "setup": { + "duration": 0.07020766660571098, + "outcome": "passed" + }, + "call": { + "duration": 0.6389470677822828, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 506, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'add_product_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'inStock': True, 'name': 'Widget...}}, 'type': 'function'}]}, 'tool_responses': [{'response': \"{'response': 'Successfully added product with id: 123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n> accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:506: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.00035757478326559067, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-get_then_create_event_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-get_then_create_event_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-get_then_create_event_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "get_then_create_event_tool" + }, + "setup": { + "duration": 0.07121358439326286, + "outcome": "passed" + }, + "call": { + "duration": 0.5222592242062092, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 506, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'get_then_create_event_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'date': '2025-03-03', ...ents found for 2025-03-03 at 10:00'}\"}, {'response': \"{'response': 'Successfully created new event with id: e_123'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n> accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:506: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.0003436664119362831, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-compare_monthly_expense_tool]", + "lineno": 471, + "outcome": "failed", + "keywords": [ + "test_chat_streaming_multi_turn_tool_calling[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-compare_monthly_expense_tool]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-compare_monthly_expense_tool", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "compare_monthly_expense_tool" + }, + "setup": { + "duration": 0.07017400953918695, + "outcome": "passed" + }, + "call": { + "duration": 1.7245550760999322, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 506, + "message": "" + }, + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 688, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\ncase = {'case_id': 'compare_monthly_expense_tool', 'expected': [{'num_tool_calls': 1, 'tool_arguments': {'month': 1, 'year': ... 'Total expenses for January 2025: $1000'}\"}, {'response': \"{'response': 'Total expenses for February 2024: $2000'}\"}]}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases.get(\"test_chat_multi_turn_tool_calling\", {}).get(\"test_params\", {}).get(\"case\", []),\n ids=case_id_generator,\n )\n def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, provider, verification_config, case):\n \"\"\" \"\"\"\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages = []\n tools = case[\"input\"][\"tools\"]\n expected_results = copy.deepcopy(case[\"expected\"])\n tool_responses = copy.deepcopy(case.get(\"tool_responses\", []))\n input_messages_turns = copy.deepcopy(case[\"input\"][\"messages\"])\n \n while len(input_messages_turns) > 0 or (len(messages) > 0 and messages[-1][\"role\"] == \"tool\"):\n if len(messages) == 0 or messages[-1][\"role\"] != \"tool\":\n new_messages = input_messages_turns.pop(0)\n if isinstance(new_messages, list):\n messages.extend(new_messages)\n else:\n messages.append(new_messages)\n \n # --- API Call (Streaming) ---\n stream = openai_client.chat.completions.create(\n model=model,\n messages=messages,\n tools=tools,\n stream=True,\n )\n \n # --- Process Stream ---\n> accumulated_content, accumulated_tool_calls = _accumulate_streaming_tool_calls(stream)\n\ntests/verifications/openai_api/test_chat_completion.py:506: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nstream = \n\n def _accumulate_streaming_tool_calls(stream):\n \"\"\"Accumulates tool calls and content from a streaming ChatCompletion response.\"\"\"\n tool_calls_buffer = {}\n current_id = None\n full_content = \"\" # Initialize content accumulator\n # Process streaming chunks\n for chunk in stream:\n> choice = chunk.choices[0]\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:688: IndexError" + }, + "teardown": { + "duration": 0.0003162780776619911, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[meta-llama/Llama-3.3-70B-Instruct-Turbo-stream=False]", + "lineno": 554, + "outcome": "skipped", + "keywords": [ + "test_chat_multi_turn_multiple_images[meta-llama/Llama-3.3-70B-Instruct-Turbo-stream=False]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-stream=False", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "stream=False" + }, + "setup": { + "duration": 0.07253758516162634, + "outcome": "passed" + }, + "call": { + "duration": 0.00021537486463785172, + "outcome": "skipped", + "longrepr": "('/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py', 561, 'Skipped: Skipping test_chat_multi_turn_multiple_images for model meta-llama/Llama-3.3-70B-Instruct-Turbo on provider together based on config.')" + }, + "teardown": { + "duration": 0.0004162406548857689, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[meta-llama/Llama-3.3-70B-Instruct-Turbo-stream=True]", + "lineno": 554, + "outcome": "skipped", + "keywords": [ + "test_chat_multi_turn_multiple_images[meta-llama/Llama-3.3-70B-Instruct-Turbo-stream=True]", + "parametrize", + "pytestmark", + "meta-llama/Llama-3.3-70B-Instruct-Turbo-stream=True", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", + "case_id": "stream=True" + }, + "setup": { + "duration": 0.07268107868731022, + "outcome": 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"meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "stream=False" + }, + "setup": { + "duration": 0.07398672867566347, + "outcome": "passed" + }, + "call": { + "duration": 4.383559702895582, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0002781357616186142, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[meta-llama/Llama-4-Scout-17B-16E-Instruct-stream=True]", + "lineno": 554, + "outcome": "failed", + "keywords": [ + "test_chat_multi_turn_multiple_images[meta-llama/Llama-4-Scout-17B-16E-Instruct-stream=True]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Scout-17B-16E-Instruct-stream=True", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "case_id": "stream=True" + }, + "setup": { + "duration": 0.08006586041301489, + "outcome": "passed" + }, + "call": { + "duration": 2.16784877050668, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 596, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 596, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Scout-17B-16E-Instruct', provider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\nmulti_image_data = ['data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGC...6pH9jaTzNv7vfRRXzubfxj9f8Pv8AkTz/AMX/ALbEz5Ly38lfMk/5Z/u64PxhqEZh+z/6rzvn2UUV5EvgPuzy/wAc6p5dt5ccibJpNkkdFFFec27mZ//Z']\nstream = True\n\n @pytest.mark.parametrize(\"stream\", [False, True], ids=[\"stream=False\", \"stream=True\"])\n def test_chat_multi_turn_multiple_images(\n request, openai_client, model, provider, verification_config, multi_image_data, stream\n ):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages_turn1 = [\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"image_url\",\n \"image_url\": {\n \"url\": multi_image_data[0],\n },\n },\n {\n \"type\": \"image_url\",\n \"image_url\": {\n \"url\": multi_image_data[1],\n },\n },\n {\n \"type\": \"text\",\n \"text\": \"What furniture is in the first image that is not in the second image?\",\n },\n ],\n },\n ]\n \n # First API call\n response1 = openai_client.chat.completions.create(\n model=model,\n messages=messages_turn1,\n stream=stream,\n )\n if stream:\n message_content1 = \"\"\n for chunk in response1:\n> message_content1 += chunk.choices[0].delta.content or \"\"\nE IndexError: list index out of range\n\ntests/verifications/openai_api/test_chat_completion.py:596: IndexError" + }, + "teardown": { + "duration": 0.0003619194030761719, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-stream=False]", + "lineno": 554, + "outcome": "passed", + "keywords": [ + "test_chat_multi_turn_multiple_images[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-stream=False]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-stream=False", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "stream=False" + }, + "setup": { + "duration": 0.0709412069991231, + "outcome": "passed" + }, + "call": { + "duration": 6.110534753650427, + "outcome": "passed" + }, + "teardown": { + "duration": 0.0002450142055749893, + "outcome": "passed" + } + }, + { + "nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_multi_turn_multiple_images[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-stream=True]", + "lineno": 554, + "outcome": "failed", + "keywords": [ + "test_chat_multi_turn_multiple_images[meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-stream=True]", + "parametrize", + "pytestmark", + "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8-stream=True", + "test_chat_completion.py", + "openai_api", + "verifications", + "tests", + "llama-stack", + "" + ], + "metadata": { + "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", + "case_id": "stream=True" + }, + "setup": { + "duration": 0.0725309094414115, + "outcome": "passed" + }, + "call": { + "duration": 2.291131243109703, + "outcome": "failed", + "crash": { + "path": "/home/erichuang/llama-stack/tests/verifications/openai_api/test_chat_completion.py", + "lineno": 596, + "message": "IndexError: list index out of range" + }, + "traceback": [ + { + "path": "tests/verifications/openai_api/test_chat_completion.py", + "lineno": 596, + "message": "IndexError" + } + ], + "longrepr": "request = >\nopenai_client = \nmodel = 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8'\nprovider = 'together'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...-versatile', 'meta-llama/llama-4-scout-17b-16e-instruct', 'meta-llama/llama-4-maverick-17b-128e-instruct'], ...}, ...}}\nmulti_image_data = ['data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGC...6pH9jaTzNv7vfRRXzubfxj9f8Pv8AkTz/AMX/ALbEz5Ly38lfMk/5Z/u64PxhqEZh+z/6rzvn2UUV5EvgPuzy/wAc6p5dt5ccibJpNkkdFFFec27mZ//Z']\nstream = True\n\n @pytest.mark.parametrize(\"stream\", [False, True], ids=[\"stream=False\", \"stream=True\"])\n def test_chat_multi_turn_multiple_images(\n request, openai_client, model, provider, verification_config, multi_image_data, stream\n ):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n messages_turn1 = [\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"image_url\",\n \"image_url\": {\n \"url\": multi_image_data[0],\n },\n },\n {\n \"type\": \"image_url\",\n 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